The sound waves bounce on objects such as the strawberry. The sound waves that bounce on the strawberry reflect back to the bat. The direction from where the sound returns tells it in which direction the strawberry is. The time taken for the sound waves to reach the strawberry and return back to the bats ear tells it how far the strawberry is. The longer the sound waves take to return, the further away is the strawberry.

So using sound, the bat can find the unseen. In a similar way, we in anaesthesia can also use sound to locate the unseen. For an example, we can use it to guide our needle when inserting a central venous catheter into the internal jugular vein in the neck of a patient without damaging other vital structures.

Sound is a vibration that is transmitted in a medium (e.g. air), that can be heard by an human ear. If you were an ant or a bird, the meaning of sound would be different. Sound specifically refers to what the human ear can hear.

All vibrations, including sound, have a frequency. Frequency is a measure of how often something vibrates per second. The unit of frequency is Hertz, the official symbol being Hz, and can be thought of as vibrations per second (this not the official definition!). The human ear can hear between frequencies of about 20 Hz to 20,000 Hz. So sound is vibrations in this frequency range.

The human ear cannot hear above 20,000 Hz. Frequencies above 20,000 Hz are called ultrasound . Bats use ultrasound to locate food as described above and dolphins use it to communicate with their friends. And importantly, ultrasound is used in anesthesia for imaging various parts of the body. Ultrasound used in medical imaging typically operate at frequencies way above human hearing: about 2 million Hz to 20 million Hz (2-20 MHz).

There is a special material called a piezo electric crystal. This material has a very special property. When a voltage is applied to an piezo electric crystal (shown in red below), it expands. When the voltage is removed, it contracts back into its original thickness.

To locate something using ultrasound, one needs to have a way of listening to the sound waves that are bounced off various objects. In the previous section, we discussed how a piezo electric crystal expands when a voltage is applied to it and how that is used to generate ultrasound waves. In addition to this, piezo electric crystals have another very useful property that enables it to be also used for receiving ultrasound waves. When a piezo electric crystal is compressed, it generates a voltage. This property is used to listen for the ultrasound waves that return after striking objects. When returning sound waves hit the piezo electric crystal, it gets compressed. The crystal then generates a voltage that corresponds to the intensity of the ultrasound wave that hits it.

The above examples show only one crystal for clarity. In reality, ultrasound probes are composed of a large number of individual piezo electric crystals. The information gathered from the crystals are processed by a computer to display the images on a screen.

Air is the enemy of ultrasound. Ultrasound waves tend to reflect strongly wherever air meets biological tissue. If there is even a small bubble between the probe and the patients skin, the ultrasound waves will be reflected away instead of penetrating the skin. Without the waves going into the patient, you will not be able to get a descent image. Therefore, it is absolutely vital to make sure that there are no air bubbles between the probe and the skin of the patient.

On the other hand, ultrasound travels very easily through liquids. For this reason, it is common to use a thick liquid ( jelly ) between the probe and the patients skin. The thick liquid helps to keep away air bubbles and allows easy passage of the ultrasound waves.

Once the ultrasound enters the body, different things can happen to them. Only some of it returns back to the probe to help the machine form a image. The rest is lost. When ultrasound enters the body, some of it undergoes:

Some of the ultrasound waves are attenuated. That is, the body absorbs the ultrasound energy, making the waves disappear. These waves dont return to the probe and are therefore wasted. The more the body tissues that the ultrasound waves have to cross, the more attenuation the waves suffer. That is one reason why it is more difficult to image deeper structures.

Every substance , such as a nerves, muscles, or fat, has a unique property called acoustic impedance. Acoustic impedance is a somewhat complicated concept, but basically depends on the density of the substance and the speed of ultrasound in that substance. Substances with different acoustic impedances alter the course of ultrasound waves in an important manner.

Part of the ultrasound waves continues into the second substance, but becomes slightly bent away from their original direction (pink arrow). The bending away when ultrasound passes from one substance to another substance with a different acoustic impedance is called refraction.

In addition to this, another very important thing happens. Part of the wave (shown as blue arrow in image below) is reflected back to the probe .The amount reflected back depends on the difference of the acoustic impedance between the two substances, more the difference, more the reflection. Reflected waves are extremely important, since it is only these waves that return back to the probe and provide information for the machine to show an image.

As the ultrasound wave crosses from one tissue to the next, each with a different acoustic impedance, some of the wave is reflected back at each crossing (two blue arrows in image below). Therefore, multiple reflected waves return to the probe and the machine uses this information to display an image showing the different tissues.

Irregular surfaced objects such as nerves scatter the ultrasound waves in all directions. A small portion of the waves are reflected back to the probe (shown as blue arrow in image below). This is called scattered reflection.

If an object is large and smooth like a nerve blocking needle, all the ultrasound wave is reflected back. This is very useful since it helps us to clearly see needles when performing ultrasound guided nerve blocks. This mirror like reflection, where the waves are reflected back mostly in one direction is officially called specular reflection.

In a similar way, there are many different ways a ultrasound probe can look at things. These ways are called modes and these will be described below. The modes are named with letters and may sound very confusing. However, we will discuss each in turn and you will, at the end, understand the basics of them.

One use of the A scan is to measure length. For an example, ophthalmologists can use it to measure the diameter of the eye ball. Imagine that the red circle below is the eye ball and you want to measure the diameter of it.

As the wave reaches the first wall of the eye, some of the ultrasound is reflected back into the probe. The returned wave is recorded on the line as a bump. The stronger is the returned wave, higher the height of the bump. The height of the bump is called Amplitude which is what the A of A scan stands for.

The time difference between the first bump and the second bump represents how long the ultrasound wave took to travel between the two walls. Longer the length, longer is the time difference. The speed of ultrasound in the eye is known to be 1500 meters per second (yes, that is fast). So if you know the time difference (given by the interval between the two bumps), you can calculate how far the wave traveled between the two walls of the eye, giving you the eyeball length.

In its simplest form, the B scan mode is very similar to the A scan mode. Just like the A scan, a wave of ultrasound is sent out in a pencil like narrow path. And again like the A scan, the horizontal line represents the time since the wave was released.

Again using the eye ball as an example, the probe is placed on one end. Like in the A scan, when the wave meets the first wall, a part of the wave is reflected back into the probe. However, this time, instead of a bump, the strength of the returning wave is recorded by a bright dot. The brightness of the dot represents the strength of the returning wave. The brighter the dot, the stronger is the returning wave. The letter B of B scan represents Brightness.

And when the wave reaches the other wall, again part of it is reflected back into the probe. This returning wave, like the returning wave from the previous wall, is represented as a bright dot on the screen.

The B scan in the form discussed doesnt amount to much . just a few dots of different brightness along a line. However, if a B scan is done at different levels of the object, you will get a two dimensional image on the screen as shown below. First a B scan is done at the top of the structure chosen, e.g. the eye.

Since the image is redrawn so rapidly, one can see size changes (e.g. pulsations of carotid artery) in real time (i.e. as it happens) .The B scan is the commonest mode of ultrasound that we see in anesthesia. The complete description of the mode is real time , 2 dimensional (2 D), B scan. The 2 dimensional (2D) refers to the fact that the image has two dimensions; horizontal (X axis) and vertical ( Y axis).

Frequency, wavelength, resolution, and depth are interlinked. These seem to be complex physics stuff to grasp, but it is worth understanding them because it can help you to get the best images from your ultrasound machine.This website will simply it all for you so dont worry.

In the image below, the high frequency wave has an higher number of cycles of high /low pressure areas in a period of one second. Similarly, the low frequency wave has a lower number of cycles of high / low areas of pressure per second.

The unit of frequency is Hertz, which is abbreviated to Hz. Hz refers to the number of cycles per second. In the image below, the low frequency has 2 high /low pressure cycles per second. In other words, the low frequency wave below has a frequency of 2 Hz. Similarly, the high frequency wave below has 4 high /low pressure cycles per second. In other words, the high frequency wave below has a frequency of 4 Hz. These small numbers have been used only to explain the concept of frequency to you. In reality, ultrasound operates at many million cycles per second. (e.g. about 2 million Hz to 20 million Hz (2-20 MHz)

Wavelength is the distance between identical points in adjacent cycles of a waveform. For an example, you can measure wavelength by measuring the distance between two adjacent highest pressure points in the wave.

Wavelength and frequency are importantly interrelated. When the frequency is increased the wavelength becomes shorter. Similarly, when the frequency is decreased, the wavelength becomes longer. This has some important implications when you use ultrasound in clinical practice and this will be explained to you later.

First let us discover why high frequency waves have a shorter wavelength. In the following image, the upper wave has a higher frequency. You will recall that high frequency means higher cycles per second (one cycle = one high pressure area followed by one low pressure area). In this example, the high frequency wave has four cycles per second (the high pressure areas are shown with a purple dot). Similarly, the lower wave has a lower frequency and therefore has less cycles per second.

This means that the high frequency wave has more cycles that are squeezed into the one second time frame. The low frequency wave has less cycles squeezed into the same one second time frame. Thus the cycles in a low frequency wave are more more wide apart. As discussed before, wavelength can be measured between two adjacent high pressure areas of a wave. You can now see that low frequency waves have a longer wavelength. High frequency waves have a shorter wavelength.

So in clinical practice, you might think that using the lowest frequency is the best because it gives a longer depth of penetration. However it isnt as straightforward as the next section will explain.

The physics of ultrasound is such that both these requirements cannot be fully met at the same time. For a good resolution, we need a short wavelength, which means we need a higher frequency. Unfortunately, an higher frequency also means a shorter penetration.

The optimum frequency is one that will give you just the adequate amount of depth necessary to see the structures of interest. You dont allow unnecessary depth which would have unnecessarily lowered the frequency.

Before proceeding to explain the Doppler effect, let us quickly revise what frequency means. Frequency is the number of oscillations of the sound wave that occurs per one second. In the example below, the high frequency has four oscillations per second and the low frequency has two oscillations per second.

Now let me try and explain the Doppler effect. As you will recall, the ultrasound machine measures the distance to things by transmitting ultrasound waves from the probe and seeing how long the wave takes to return back to the probe.

The transmitted wave has a certain frequency. The wave that returns to the probe also has a certain frequency. When the wave is bounced back from a stationary object such as a nerve, both the transmitted and the returned waves have the same frequency.

However, if we repeat the same thing with an object moving towards the probe, something interesting happens. Imagine that the red disc below is an red blood cell moving towards the probe ( I know it is a rather large red blood cell !).

This ultrasound wave reaches the moving red blood cell and bounces back. However, this time if you measured the frequency of the returned wave, it will not be the same as the frequency of the transmitted wave. The wave that bounces off an object moving towards the probe will have a higher frequency than the frequency of the wave transmitted from the probe. This is becuase the moving object squashes the waves as it moves towards the probe (see diagram below) . This is an example of the doppler effect. When a wave is sent to an object that is moving towards the transmitting probe, the doppler effect makes the frequency of the returning wave to be higher than the frequency of the wave sent out. The faster the object moves towards the transmitting probe, the higher will be the difference.

Ultrasound imaging devices can use the doppler effect to help us in many ways, including helping us to identify blood vessels.When you scan structures without using the doppler effect, the machine simply sees how long the waves take to return back to the probe and constructs an image.

However, this image does not clearly show which of the circles is a blood vessel and which is a nerve. Fortunately, blood vessels have one big difference from nerves. Blood vessels are full of rapidly moving red blood cells. When using an ultrasound machine with the ability to look for the doppler effect, the machine constructs an image in the usual way by seeing how long waves take to return back to the probe. But in addition, it also analyses the frequency of the returned waves. Whenever the returned wave has a frequency different to the frequency of the transmitted wave, the machine knows that the place where those waves bounced back from have moving objects. To help you to see these areas of moving cells, the ultrasound machine adds colour to areas showing the doppler effect. In the image below, this helps you to differentiate nerve from blood vessels. The nerve has no moving cells, so there is no doppler effect and therefore no colour is added by the machine. The blood vessel has rapidly moving cells which cause a doppler effect, and where this occurs, the machine adds the colour red to help you identify it. This makes it easy for you to identify the blood vessel.

The compression of the wave into a smaller length means that the oscillations of high and low pressure areas of the wave become more concentrated. As the wave gets compressed, it has more oscillations (high pressure / low pressure areas) per second than before ( i.e. the frequency has increased). This explains how the frequency of waves reflected from objects moving towards the probe have an higher frequency than the frequency of the wave sent out.

The Doppler effect also occurs for objects moving AWAY from the transmitting probe. Again there is a difference between the frequency of the transmitted waves and the frequency of the returning waves. However this time , the returning waves have a LOWER frequency than the frequency of the waves transmitted. The faster the object moves away, the greater will be the frequency difference. The reason for this decrease in frequency is the opposite of the explanation given before. In this case, the object moving away stretches the wave. The stretching reduces the number of oscillations per one second.

Application notes Case studies Measure shock & vibration Measure strain Measure voltage Measure current Measure temperature with thermocouples Measure temperature with RTD Measure weight with load cells

In the field of signal processing, a filter is a device that suppresses unwanted components or features from a signal. The most commonly used filters are low-pass, high-pass, band-pass and band-stop. Characteristics that describe filter are its type, cutoff frequency, order (steepness).

Let's start with some terminology followed by a basic introduction. Then we are going to acquaint ourselves with analog and digital filters, as well as converters and do a comparison of the two. Next on the list will be the different types of filters and their uses. Finally, we are going to see how to setup and use all of these filters in Dewesoft X.

In the field of signal processing, a filter is a device or process that, completely or partially, suppresses unwanted components or features from a signal. This usually means removing some frequencies to suppress interfering signals and to reduce background noise.

Analogue filters are a basic block of signal processing and are designed to operate on a continuously signal (the signal has the value at every instance of time). They are electronic circuits that are dependent on the elements used:

They can be linear or non-linear, depending on the type of the equation that describes them. Most analog filters have an infinite impulse response or IIR (this means that the impulse response of the filter, in theory, will never reach zero but in real applications the signal will approach zero and can be neglected). This is because the analog circuit consists of resistors, capacitors, and/or inductors and the latter have a "memory" and their internal state never really relaxes after an impulse.

A digital filter is a signal processing system that performs mathematical operations on a sampled (a continuous signal that has been reduced to a discrete one), discrete-time (unlike the continuous signal, the discrete one does not have a value at every instance of time - it is quantized ), digital (a physical signal that is a representation of a sequence of discrete values - for example, an arbitrary bit stream or a digitized analog signal) signal.

Analogue filters are much more subjected to non-linearity (resulting in smaller accuracy) because the electronic components that are used for filtering are inherently imperfect and often have values specified to a certain tolerance limit (resistors often have a tolerance of 5% ). This can be further affected by temperature and time changes. The elements of the circuits also introduce thermal noise, because every element is subject to heating. As we might expect, the more complex the circuit, the greater the magnitude of component errors. On top of that, analogue filters cannot have a FIR response because it would require delay elements.

But where they shine is high-frequency filtering, low latency, and speed. Like it was mentioned in the converter section, a digital filter can not work without preemptive anti-alias filtering, which can only be achieved with a low-pass analog filter. The speed of the analog filter can easily be 10 to 100 times that of a digital one. Also worth mentioning is that in very simple cases, an analog filter surpasses its digital counterpart in terms of cost efficiency.

The digital filter shines in a lot of areas where the analog does not. It is more accurate, supports both IIR and FIR, it can be programmed, making them easier to build and test while giving them greater flexibility. It's also more stable since it isn't affected by temperature and humidity changes. They are also far superior in terms of cost efficiency, especially as the filter gets more complex.

The downsides of digital filters are latency because the signal has to go through two converters and still be processed at high frequencies. In today's modern circuits, both filters are used to complement each other and achieve maximum speed and accuracy.

Now that you learned the basics of filters and how they work, we are going to focus only on digital filters and different digital filter types, since the main goal of this tutorial is to teach you how to use them in Dewesoft.The next chapters will show you how to set appropriate filters in Dewesoft, how to use them and explain what their purpose is. But first, a simple example will show you the usage of filters.Using filtersLet's find out why we should even use filters. For this example, I have a tuning fork instrumented with strain gage connected on Dewesoft measuring device SIRIUS.Image 8: DAQ device with strain gage sensor mounted on a tuning forkWhen the tuning fork is connected to the STG module, it can be seen in Measure mode under Channel setupscreen's Analog in section.Image 9: Analog in channel setupIf you take a look at the recorder when a static force is applied on the tuning fork, you can see the changing offset of the signal.You might also try hitting the tuning fork so it makes a sound that is reflecting the natural frequency of the tuning fork (the conventional way it is used). You can see a high-frequency vibration with falling amplitude because of air friction and friction in the fork.Image 10: Signal of different forces applied to the tuning forkWhen looking at the FFT screen (change it to the logarithmic scale to see all the amplitudes), you can see that there is an obvious peak at approximately 440 Hz. You can also place a cursor at this point by simply clicking on the peak in the FFT. The frequency shown is 439.5 Hz. It is not exactly 440 Hz because FFT has a certain line resolution.This line resolution depends on the sampling rate and the number of lines chosen for the FFT. If you want to have a faster response on the FFT, we would choose fewer lines, but you would have a lower frequency resolution. If on the other hand, you want to see the exact frequency, it is necessary to set a higher line resolution. This is well described in the reference guide, but a simple rule of thumb is: if it takes 1 second to acquire the data from which the FFT is calculated, the resulting FFT will have 1 Hz line resolution. If you acquire data for 2 seconds, line resolution will be 0.5 Hz.Image 11: FFT and time signal of the vibrating tuning forkThis is also a perfect example to learn about using the filters in Dewesoft X. Clearly, there is one part of the signal in the form of the offset (static load) and one part in a form of dynamic ringing with a 440 Hz frequency.If you want to extract those two components from the original waveform, you need to set two filters - one low pass and one high pass.To achieve this add two filters in the Channel setup's Math module.Image 12: Math made filters1. Setting the 1st filterSet by selecting the input channel, in this case, Tuning forkLeave Design type at PresetIn Design parameters confirm that the Prototypeis set to Butterworthand set the number of Orders to 6.Set the Frequencies filter Type to Low pass, and update Fc2 to 200HzThis filter will pass all the signals below 200Hz frequency. All the frequencies above 200Hz will be cut off.2. Setting the 2nd filterSet by selecting the input channel, again to, Tuning forkLeave Design type at PresetIn Design parameters confirm that the Prototypeis set to Butterworthand set the number of Orders to 6.Set the Frequencies filter Type to High pass, and update Fc1 to 200HzThis filter will block all the signals below 200Hz frequency and pass by all signals above 200Hz.If you display those two filters on the recorder, you can see that the signal is nicely decomposed to the static load and dynamic ringing. You can use this technology to cut off unwanted parts of the signal or to extract wanted frequency components of the certain signal.Image 13: Comparison of RAW, Low-pass filtered and High-pass filtered time signals

You might also try hitting the tuning fork so it makes a sound that is reflecting the natural frequency of the tuning fork (the conventional way it is used). You can see a high-frequency vibration with falling amplitude because of air friction and friction in the fork.

When looking at the FFT screen (change it to the logarithmic scale to see all the amplitudes), you can see that there is an obvious peak at approximately 440 Hz. You can also place a cursor at this point by simply clicking on the peak in the FFT. The frequency shown is 439.5 Hz. It is not exactly 440 Hz because FFT has a certain line resolution.

This line resolution depends on the sampling rate and the number of lines chosen for the FFT. If you want to have a faster response on the FFT, we would choose fewer lines, but you would have a lower frequency resolution. If on the other hand, you want to see the exact frequency, it is necessary to set a higher line resolution. This is well described in the reference guide, but a simple rule of thumb is: if it takes 1 second to acquire the data from which the FFT is calculated, the resulting FFT will have 1 Hz line resolution. If you acquire data for 2 seconds, line resolution will be 0.5 Hz.

This is also a perfect example to learn about using the filters in Dewesoft X. Clearly, there is one part of the signal in the form of the offset (static load) and one part in a form of dynamic ringing with a 440 Hz frequency.If you want to extract those two components from the original waveform, you need to set two filters - one low pass and one high pass.

If you display those two filters on the recorder, you can see that the signal is nicely decomposed to the static load and dynamic ringing. You can use this technology to cut off unwanted parts of the signal or to extract wanted frequency components of the certain signal.

FIR filter uses only current and past input digital samples to obtain a current output sample value. It does not utilize past output samples.FIR filter calculates in the way that it takes the input channel data and multiplies it with the specific curve. The easiest example of FIR filter is the averaging filter. Let's say we want to average the input data over three samples. The equation would be like this:Our simple average filter would have a result like this:Image 14: Unfiltered and FIR filtered signalThe obvious disadvantage is that we would need a lot of input points to be able to filter the data sharp.IIR filter uses current input sample value, past input, and output samples to obtain current output sample value. We could rewrite the formula like this:So we are using the previous sample to basically perform exponential averaging, but this very simple and efficient formula will filter the data much more:Image 15: Unfiltered and IIR filtered signalComparison of both:Image 16: Unfiltered, IIR filtered and FIR filtered time signalsBy the way, these functions were simulated in a Dewesoft X formula by using prev and .data functions:Formula #1.Image 17: Math formulas for IIR filter and FIR filter of noiseInfinite and finite impulse responseLet's first take a look at the advantages and disadvantages of the IIR response, then the FIR response and finish with a quick summary and overview of both.The main advantage of IIR filters over FIR filters is their efficient implementation, which helps them to meet specifications in terms of pass-band, stop-band, ripple and/or roll-off. For this, we require a lower order IIR filter, compared to a similar FIR filter. This effectively corresponds to fewer calculations needed, resulting in rather large computational savings. The best use of IIR filters is when the linear characteristics are not of concern and for lower order tapping.The main disadvantages of IIR are instability, feedback, non-linearity and has limited cycles.FIR, on the other hand, can make linear characteristics always possible, requires no feedback, is inherently stable, can be easier to design for meeting particular frequency responses and does not have limited cycles. The disadvantage of FIR filters is that they require more memory and computing power than an IIR with similar sharpness or selectivity, particularly when it comes to lower order tapping.The following table offers a quick comparison of IIR and FIR filter:IIRFIRimpulse response is infiniteimpulse response is finitecan be unstablealways stablesharp cut-offslowIIR is recursiveFIR is non-recursivemulti-rate signals are not supported by IIRmulti-rate signals are supported in FIRphase response is not linearphase response is always linearIIR is less accurateFIR is more accurateIIR transfer function consists of zeros and polesFIR transfer function consists only of zerosIIR requires less computing power than FIRFIR requires more computing power that IIRcan have limited cyclesno limited cycles

FIR filter calculates in the way that it takes the input channel data and multiplies it with the specific curve. The easiest example of FIR filter is the averaging filter. Let's say we want to average the input data over three samples. The equation would be like this:

The main advantage of IIR filters over FIR filters is their efficient implementation, which helps them to meet specifications in terms of pass-band, stop-band, ripple and/or roll-off. For this, we require a lower order IIR filter, compared to a similar FIR filter. This effectively corresponds to fewer calculations needed, resulting in rather large computational savings. The best use of IIR filters is when the linear characteristics are not of concern and for lower order tapping.

FIR, on the other hand, can make linear characteristics always possible, requires no feedback, is inherently stable, can be easier to design for meeting particular frequency responses and does not have limited cycles. The disadvantage of FIR filters is that they require more memory and computing power than an IIR with similar sharpness or selectivity, particularly when it comes to lower order tapping.

Here you will learn how to add a new filter using Dewesoft X.A new filter can be added inside the Dewesoft X Math module by clicking the Add math button and selecting the appropriate filter from the drop-down menu. You can help yourself by using the search bar, by simply entering FIR, IRRor Frequency domain filter.We can select the filter type from the following options:IIR filter (IIR - Infinite Impulse Response)FIR filter (FIR - Finite Impulse Response)Frequency domain filter

A new filter can be added inside the Dewesoft X Math module by clicking the Add math button and selecting the appropriate filter from the drop-down menu. You can help yourself by using the search bar, by simply entering FIR, IRRor Frequency domain filter.

IIR filters are digital filters with the infinite impulse response, that means that the response to the impulse will be non-zero over an infinite length of time. It supports low pass, high pass, bandpass and bandstop type of filters and multiple input channels. You can select from Chebyshev, Butterworth and Bessel prototypes of a filter. A drawback of the IIR filter is that the phase is not linear.First, add a new IIR filter in a math section.In Measuremode go to Channel setup and open the Math module. Search for IIR filtertab and open it.When you select the IIR filter, the following IIR Filter setup window will open. You can always access this setup window by clicking on the Setup button. On the left side of the setup screen, we have to select the input channel on which the filter will be applied. If we want to apply the same filter to multiple channels we can do that just by selecting all the channels we want to have filtered.Image 18: Math formulas for IIR filter and FIR filter of noiseIn IIR filter setup we can select between two Design types:Preset filter (Opened by default)Manual filter

When you select the IIR filter, the following IIR Filter setup window will open. You can always access this setup window by clicking on the Setup button. On the left side of the setup screen, we have to select the input channel on which the filter will be applied. If we want to apply the same filter to multiple channels we can do that just by selecting all the channels we want to have filtered.

The order of the filter defines the steepness of the filter. For the Butterworth the roll-off is (-20 dB/decade)*order, so for the sixth order the roll-off would be -120 dB/decade. That would mean if the amplitude at 100 Hz (already in the stop band) is 1, the amplitude at 1000 Hz would be 10(-120/20)=10-6.

Fc1 value must be always lower than Fc2. These values are limited by filter stability. In Dewesoft X, the filters are calculated in sections, which enable the ratio between cutoff and sample frequency in a range of 1 to 100000. So we are able to calculate 1 Hz high pass filter with a 100 kHz sampling rate.

Scale factor means the final multiplication factor before the value is written to an output channel. It helps us to change the unit, for example. A good example of using the Scale is shown in the Integration section.

This page will offer a brief summary of the three different IIR filter prototypes in Dewesoft X.ButterworthThe Butterworth filter is a type of signal processing filter designed to have a maximally flat frequency response (having no ripples) on the pass-band and rolls off towards zero in the stop-band. It is also referred to as a maximally flat magnitude filter and offers the smoothest possible curve of any class of filter of the same order.Image 20: Butterworth filter frequency responseChebyshevChebyshev filters have a steeper roll-off and more pass-band ripple (type I) or stop-band ripple (type II) than Butterworth filters. Chebyshev filters have the property that they minimize the error between the idealized and the actual filter characteristic over the range of the filter, but with ripples in the passband. Because of the passband ripple inherent in Chebyshev filters, the ones that have a smoother response in the passband but a more irregular response in the stopband are preferred for some applications.Image 21: Chebyshev filter frequency responseBesselBessel filter is a type of a linear filter with a maximally flat group delay (maximally linear phase response) over its pass-band. The result is a linear phase response, meaning that the passing waveforms will be minimally distorted. It is often used in audio crossover systems. Analog Bessel filters are characterized by almost constant group delay across the entire passband, thus preserving the wave shape of filtered signals in the passband.Image 22: Bessel filter frequency response

The Butterworth filter is a type of signal processing filter designed to have a maximally flat frequency response (having no ripples) on the pass-band and rolls off towards zero in the stop-band. It is also referred to as a maximally flat magnitude filter and offers the smoothest possible curve of any class of filter of the same order.

Chebyshev filters have a steeper roll-off and more pass-band ripple (type I) or stop-band ripple (type II) than Butterworth filters. Chebyshev filters have the property that they minimize the error between the idealized and the actual filter characteristic over the range of the filter, but with ripples in the passband. Because of the passband ripple inherent in Chebyshev filters, the ones that have a smoother response in the passband but a more irregular response in the stopband are preferred for some applications.

Bessel filter is a type of a linear filter with a maximally flat group delay (maximally linear phase response) over its pass-band. The result is a linear phase response, meaning that the passing waveforms will be minimally distorted. It is often used in audio crossover systems. Analog Bessel filters are characterized by almost constant group delay across the entire passband, thus preserving the wave shape of filtered signals in the passband.

Let's look at the direct comparison between FIR and IIR filter.Again, let's do the demo with a tuning fork. We connect the forks to STG module. Forks natural frequency is approximately 440 Hz, so set both filters as high-pass filters with cutoff frequency at 200 Hz.Excite the fork so that it vibrates at its natural frequency.Image 26: Tuning fork amplitude over timeWhen you apply FIR filter you can see that there is no phase delay.Image 27: Aligned phase of Raw signal and FIR filtered signalWhen you apply the IIR filter in the signal, you can clearly see the phase delay.Image 28: Visible phase delay between Raw signal and IIR filtered signal

Again, let's do the demo with a tuning fork. We connect the forks to STG module. Forks natural frequency is approximately 440 Hz, so set both filters as high-pass filters with cutoff frequency at 200 Hz.

On the lower side, you see some useful information about the chosen filter. First is the Response curve.Image 29: Filter frequency response curve exampleThe red curve shows the amplification/attenuation of the filter in dB related to the frequency. To refresh the memory, dB scaling is calculated with equation a[dB] = 20 * log10(A), so the attenuation ratio is calculated with A=10(a/20).If we read out the value of -34 dB as attenuation, the ratio between input at output at that frequency will be A = 10(-34/20) = 0,02. So if the input is 1 V sine wave, the output will be 0,02 V sine wave.The phase (green curve) shows the delay of the signal in degrees.The lower table shows the coefficients with which the filters will be calculated. The filter is split into several sections for increased stability, so the result from the first section is taken to the next section and so on. These coefficients can be also copy/pasted with the right mouse click on the table to be used from/in other calculation programs.Image 30: Table of filter coefficientsZeroes & poles diagram shows the position of filter zeroes and filter poles and can suggest the stability of the filter.Image 31: Filter Zeroes and Poles

The red curve shows the amplification/attenuation of the filter in dB related to the frequency. To refresh the memory, dB scaling is calculated with equation a[dB] = 20 * log10(A), so the attenuation ratio is calculated with A=10(a/20).

If we read out the value of -34 dB as attenuation, the ratio between input at output at that frequency will be A = 10(-34/20) = 0,02. So if the input is 1 V sine wave, the output will be 0,02 V sine wave.

The lower table shows the coefficients with which the filters will be calculated. The filter is split into several sections for increased stability, so the result from the first section is taken to the next section and so on. These coefficients can be also copy/pasted with the right mouse click on the table to be used from/in other calculation programs.

When you press FIR filter the following FIR filter setup window will open. On the left side of the setup screen, you have to select the input channel on which the filter will be applied. If you want to apply the same filter to multiple channels you can do that just by selecting all the channels you want to filter.

Since Dewesoft X has the calculation delay, we can use the trick to compensate the filter delay and have absolutely no phase shift in pass as well as in the transition band of the filter. This is a major benefit compared to the IIR filter where we always have a phase shift.

When a Kaiser Window type is selected, a new Ripple field appears on the right side of the Window type field. In this field, you can enter ripple value in dB. It tells the maximum allowed passband band ripple of the filter. The higher this value is, the bigger will be non-linearity in the passband, but the filter will be stepper.

Fc1 value must be always lower than Fc2. These values are limited by filter stability. In Dewesoft X, the filters are calculated in sections, which enable the ratio between cutoff and sample frequency in a range of 1 to 100000. So we are able to calculate 1 Hz high pass filter with 100 kHz sampling rate.

When acquiring analog data with sharp transitions (like square waves) with 24 bit signal-delta ADCs (with an antialiasing filter), we can see some ringing. To eliminate ringing, or at least smooth it out, a FIR filter can be used.

The Custom defined filter setup requires:number of sections and coefficients,scale factor means the final multiplication factor before the value is written to output channel - for example, it can be used to change the unit,individual filter coefficients value.An FIR filter consists of a single section because it is stable by definition. IIR filters can have several sections.You define the number of coefficients per section which are a number of rows in the table. This basically defines the filter order.Image 38: Comparison of 1st order and 10th order filterThe last things to define are the filter coefficients. Enter a(input) and b(recur.) values in the z-plane and press the Update button to change the filter settings. You can also copy/paste the coefficients from the clipboard by right-clicking and choosing 'Copy to clipboard' or 'Paste from clipboard' menu item.Image 39: Table of custom coefficients

where fs is the sampling frequency. The upper equation reveals an important fact of filters defined in the z plane - they work only for one sample rate. Therefore, if you need the filters at different sampling rates, the coefficients need to be recalculated.

The first third of the equation is valid for z0 coefficient, second third for z-1 and the third one for z-2. The upper part of the general formula for the second-order filter (with g coefficients) is valid for the input part while the lower part (with h coefficients) is valid for recursive part of the equation.If you need a higher order filter, you need to make the equation similar to the upper equation with a larger number of the coefficients. The result will have also z-3 factor.

First, you do the pre warping. You can use a technique called pre warping to account for the nonlinearity and produce a more faithful mapping. The warping effect changes the band edges of the digital filter relative to those of the analogue filter in a nonlinear way.

Finally, you set the number of coefficients to 3, the number of sections to 1 and enter 6 calculated values in the table and press Update. All entered values are coloured red and button Update also flashes until Update is pressed.

But you still have to normalize all the numbers to z0 in b(recur.) column (number in row z0 and in column b(recur.) must be set to 1)! To get the right coefficients, all other numbers must be divided with that particular number.

It is possible to import a custom filter from Matlab (registered trademark of MathWorks company).To enter the Filter Design & Analysis Tool in Matlab, write fdatool in the Command window.Image 43: Opening Matlab Filter Design & Analysis ToolAfter that, the filter design window will show.Image 44: Matlab Filter Design & Analysis ToolDesign the IIR filter in Matlab and then click on the filter coefficients button. Filter coefficients should appear in SOS matrix form. That is how they are presented in Dewesoft X.Image 45: Matlab Filter Design & Analysis ToolIn Dewesoft X, you can't enter the scale factors so you just have to include them in the filter. One section in Dewesoft X equals one SOS section in Matlab. All you have to do is scale it the right way. First three coefficients in Matlab are input and are calculated by multiplying them with the coefficient by the corresponding scale factor. The second three coefficients are recursive and all you need to do is just to copy them from b.These are calculation formulas for a specific section i:a(input)b(rekur.)z0Scale i * Section i(1)Section i(4)z-1Scale i * Section i(2)Section i(5)z-2Scale i * Section i(3)Section i(6)The coefficients for our example are calculated below:Image 46: Example coefficients

In Dewesoft X, you can't enter the scale factors so you just have to include them in the filter. One section in Dewesoft X equals one SOS section in Matlab. All you have to do is scale it the right way. First three coefficients in Matlab are input and are calculated by multiplying them with the coefficient by the corresponding scale factor. The second three coefficients are recursive and all you need to do is just to copy them from b.

The red response curve shows the amplitude damping of the filter. The amplification ratio is expressed in dB (similar to IIR filter). The green curve shows the phase delay. In the passband as well as in the transition band the phase delay is always zero and in the stopband the phase angle is not even important because of high damping ratio.Image 47: Response curve of the filter

The other display is the display of Coefficients. The upper graph shows the filter coefficients with which the raw data is convoluted. The lower graph shows the response of the filter to the step signal.

Let's look at the difference of the FIR filter compared to the standard IIR filter. Let's take a very simple 20 Hz second order filter (at 1 kHz sampling rate).The IIR filter is calculated with 6 coefficients while similar FIR filter is calculated with 40 coefficients for the same damping. Therefore, the FIR filter is more CPU (central processing unit) demanding for the same performance.Another fact is while we can get ratios of cutoff frequency to sample rate of 1/100000 and more, we can achieve only limited results with FIR filter. The ratio increases with higher number of coefficients.Image 49: Comparison of IIR and FIR filtersLet's look at the response graph at 20 Hz (exactly at the limit). The green curve is the original sine wave while the red curve one is calculated with IIR filter. We can clearly see the phase delay of the output.The blue curve is the response of the FIR filter which has absolutely no phase shift. For lots of applications, it is very important that the signals are not delayed and there the use of FIR filters is very advantageous.

The IIR filter is calculated with 6 coefficients while similar FIR filter is calculated with 40 coefficients for the same damping. Therefore, the FIR filter is more CPU (central processing unit) demanding for the same performance.

Another fact is while we can get ratios of cutoff frequency to sample rate of 1/100000 and more, we can achieve only limited results with FIR filter. The ratio increases with higher number of coefficients.

Let's look at the response graph at 20 Hz (exactly at the limit). The green curve is the original sine wave while the red curve one is calculated with IIR filter. We can clearly see the phase delay of the output.

The blue curve is the response of the FIR filter which has absolutely no phase shift. For lots of applications, it is very important that the signals are not delayed and there the use of FIR filters is very advantageous.

Add a new IIR filter in a math section.In Measuremode go to Channel setup and open the Math module. Search for Frequency domain filtertab and open it.When you add the filter or you press the Setup button on the Frequency domain filter line, the following setup window will open. On the left side of the setup screen, we have to select the input channel on which the filter will be applied. If we want to apply the same filter to multiple channels we can do that just by selecting all the channels we want to filter.Image 50: Example of IIR filter Input selection boxFrequency domain filter descriptionThis filter is quite different than the other types of filters. While IIR and FIR filters are time-domain filters, frequency domain filter calculates the spectrum of the signal with a specific number of lines and overlap and then extracts the RMS value of a certain range of this signal. Therefore, the result is not the full curve, but only one value per frequency spectrum.The usage of this filter is to extract low peaks of signals where there are big harmonics nearby where it wouldn't be possible to choose IIR filter which would extract low amplitude.The example below shows the electromotor winding failure which can be seen as low values at the rotation frequency where the line frequency is very high:Image 51: Example of EM. winding failure

When you add the filter or you press the Setup button on the Frequency domain filter line, the following setup window will open. On the left side of the setup screen, we have to select the input channel on which the filter will be applied. If we want to apply the same filter to multiple channels we can do that just by selecting all the channels we want to filter.

This filter is quite different than the other types of filters. While IIR and FIR filters are time-domain filters, frequency domain filter calculates the spectrum of the signal with a specific number of lines and overlap and then extracts the RMS value of a certain range of this signal. Therefore, the result is not the full curve, but only one value per frequency spectrum.

This defines the resolution of the filter as well as the number of points in the calculation. The resolution needs to be high enough so that the wanted harmonic can be clearly extracted, but not too high to have a lower result update rate.

Rectangular- the rule of thumb is when we want a pure transformation with no window's side effects (for advanced calculations), we should use a Rectangular window (which is, by the way, equal to no window).

Hamming, Hanning - for general purpose, Hamming or Hanning windows are commonly used because they provide a good compromise between falloff and amplitude error (maximum of 15%). This comes from the fact that old frequency analysers didn't have that many possibilities in terms of frequency lines and these two windows have narrow sideband.

Flat top - if correct amplitudes are searched, we should use the flat-top window. The amplitudes would be wrong by only a fraction (as low as 1%). Of course, there is a penalty - neighbour frequencies are also very high (sideband width is high). Flat top window is most suitable for calibration. But here it is the same: with modern equipment with lots of lines, this is no longer that much of a problem.

When tracking is selected as a frequency source we have to also define the number of harmonics. The number of harmonics describes how many harmonics need to be extracted from the spectrum. If we enter a value of 5, there will be 5 channels created for each input channel. The first channel will have the centre frequency as the frequency channel, second will have twice the frequency of the input and so on.

If Custom filter is selected from the Filter type, then we can design our own filter. With this option, we can create any type of filter curve in the frequency domain and calculate the RMS value. Sometimes it is not easy to define filter characteristics in the time domain, but we have it defined in the frequency domain. Custom FFT filter is perfect for such case.Image 52: Example of a custom FFT filterIf the frequency source is external, we can define the channel where the frequency is defined and the filter will change the characteristics to always filter correctly like in the time domain. This is especially useful for example for CA noise calculation on the external clock.

If the frequency source is external, we can define the channel where the frequency is defined and the filter will change the characteristics to always filter correctly like in the time domain. This is especially useful for example for CA noise calculation on the external clock.

Mathematical operations such as derivation and integration are also a kind of a filter. Derivation is a kind of a low-pass filter and integration is a high-pass filter which can also filter DC component.DerivationThe derivative of a function of a real variable measures the sensitivity to change of a quantity (a function or dependent variable) which is determined by another quantity (the independent variable).For example, the derivative of the position of a moving object with respect to time is the object's velocity: this measures how quickly the position of the object changes when time is advanced.The derivative of a function can be geometrically interpreted as the slope of the curve of the mathematical function f(x) plotted as a function of x. But its implications for the modelling of nature go far deeper than this simple geometric application might imply. Its importance lies in the fact that many physical entities such as velocity, acceleration, force and so on are defined as instantaneous rates of change of some other quantity. The derivative can give you a precise instantaneous value for that rate of change and lead to precise modelling of the desired quantity.The derivate of the function f(x) evaluated at x=a gives the slope of the curve at x=a.Image 53: Visual representation of derivationDouble derivationDouble derivation of a function f is the derivative of the derivative of f. The second derivative measures how the rate of change of a quantity is itself changing. For example, the second derivative of the position of a vehicle with respect to time is the instantaneous acceleration of the vehicle or the rate at which the velocity of the vehicle is changing with respect to timeIntegrationThe integral of the function f(x) over the range x=b to x=c gives the area under the curve between those points.Image 54: Visual representation of integrationThe integral of a function can be geometrically interpreted as the area under the curve of the mathematical function f(x) plotted as a function of x.The integral gives you a mathematical way of drawing an infinite number of blocks and getting a precise analytical expression for the area. That's very important for geometry - and profoundly important for the physical sciences where the definitions of many physical entities can be cast in a mathematical form the area under a curve. The area of a little block under the curve can be thought of as the width of the strip weighted by (i.e., multiplied by) the height of the strip.For example, finding the centre of mass of a continuous body involves weighting each element of mass by its distance from an axis of rotation, a process for which the integral is necessary if you are going to get a precise value. A vast number of physical problems involve such infinite sums in their solutions, making the integral an essential tool for the physical scientist.Double integrationDouble integration is useful mainly to directly integrate displacement from acceleration, so use it for dynamic signals

The derivative of a function can be geometrically interpreted as the slope of the curve of the mathematical function f(x) plotted as a function of x. But its implications for the modelling of nature go far deeper than this simple geometric application might imply. Its importance lies in the fact that many physical entities such as velocity, acceleration, force and so on are defined as instantaneous rates of change of some other quantity. The derivative can give you a precise instantaneous value for that rate of change and lead to precise modelling of the desired quantity.

Double derivation of a function f is the derivative of the derivative of f. The second derivative measures how the rate of change of a quantity is itself changing. For example, the second derivative of the position of a vehicle with respect to time is the instantaneous acceleration of the vehicle or the rate at which the velocity of the vehicle is changing with respect to time

The integral gives you a mathematical way of drawing an infinite number of blocks and getting a precise analytical expression for the area. That's very important for geometry - and profoundly important for the physical sciences where the definitions of many physical entities can be cast in a mathematical form the area under a curve. The area of a little block under the curve can be thought of as the width of the strip weighted by (i.e., multiplied by) the height of the strip.

For example, finding the centre of mass of a continuous body involves weighting each element of mass by its distance from an axis of rotation, a process for which the integral is necessary if you are going to get a precise value. A vast number of physical problems involve such infinite sums in their solutions, making the integral an essential tool for the physical scientist.

Derivation and double derivation is used, as the name already suggests, to calculation derivation of chosen input signals. Here the application range is not divided into two areas since the procedure is similar in all cases. The basic calculation is simple: you subtract the current value from the previous one and divide by the time interval.

However, this might produce very noisy signals, especially with high sampling frequency. You can look at the derivation of the 20 dB/decade growing filter in the frequency domain. Sometimes it is, therefore, nice to cut the high-frequency contents. You can enable an option to Filter high frequencies and set up the filter by choosing the Order(at least 2 for derivation and at least 3 for double derivation) and High - cutoff frequencies of the signal.

Let's assume the example of calculating acceleration out of the speed of the vehicle. With a car it is a fact that you can't have the acceleration higher than for example 10 Hz coming from the real vehicle acceleration - higher values are basically vibrations. With this in mind, you can choose the 10 Hz as the Highfrequency and just get the real vehicle acceleration.

Scale- scaling factor is similar, here the input is divided into second for derivation and divided with second squared for double derivation. So let's calculate the scaling factor for the example above:

There are two main purposes of integration: first is to get for example traveled distance from a measured velocity or to get the energy from a measured power. Another type of integration is to calculate the velocity vibration from the measured acceleration.

A clear difference between two basic functions is that in the first application the carrier of information is the DC value - the offset (traveled distance or spent energy). In the other application, the offset is only a measurement error and the carrier of information is the dynamic part of the signal - vibration velocity in our example.

Let's first take a look at how to integrate distance from velocity. If you have for example velocity in km/h as an input channel, you select it and switch OFF the option to Filter low frequencies and DC. Now the only thing left is to define the Scale. You have to know that integration adds a sec to the unit

Example: So if you have km/h as input, you have km/h * sec = 1000 m / 3600 sec * sec= 0.278 m. Therefore, if you want to have the output in meters, you have to enter 0.278 as the scale factor. If you want to have the output in km, you have to enter 0.000278. Let's practice on another example - if you have input channel as power in kW, you have at output: kW * sec = kW * 1/3600 * h = 0.000278 kWh - this is our scaling factor.

The second application is to use integration on dynamic signals like vibration acceleration. If you have measured acceleration, there is always some offset because of the amplifier and AD converter offset. This offset will result in a drift of result, which is not wanted, in this case. Therefore, we need to use the option Filter low frequencies and DC to cut this offset.

Then you need to define the Order. Be aware that integration is equivalent to a filter with order 1, so you need to choose a high pass filter with order 2 or higher to really cut DC values. The Low tells you where the resulting velocity will be cut off. High values of Low (like 10 Hz) will result in very fast stabilization in the case of overload, but it might on the other side already cut the information we require. Low values (< 1Hz) will result in quite slow stabilization times (typically 510 seconds for 1 Hz filter) but will pass through virtually entire frequency range.

For vibration measurement, a usual value is from 3 to 10 Hz for general purpose measurement. For low-frequency vibration like a human body or building vibration, a value between 0.3 and 1 Hz is used. For special application, like seasickness or high structures like TV towers or cranes, that move at a very low frequency, 0.10.3 Hz is used. But you have to know that the stabilization will be a very long process. In this case, you also need to make sure that the sensors you are using have such low-frequency range. General-purpose ICP sensors have the cutoff frequency between 0.3 to 1 Hz and, therefore, are not useful in such applications

Let's look at the Scale - scaling factor for this application. Let's assume that you measure the acceleration in g. If you want the results in mm/s, you need to have a scaling factor: 1 g * sec = 9,81 m/sec/sec * sec = 9,81 m/s = 9810 mm/s. So you need to enter 9810 in the field scaling.

Double integration is useful mainly to directly integrate displacement from acceleration, so to use it for dynamic signals. Your output unit will be multiplied by sec*sec. Therefore you need to choose again the option to Filter low frequencies and DC, but you have to take care since the double integration is similar to a second-order filter, you need to choose order 3 or higher for low-frequency filter.

Add a 2D graph and apply on the cross-correlation. Select the Complex presentation as Phase (deg). Zoom in to get the phase at the frequency of the shaker (in this case, it was 50 Hz). According to the theory, the phase shift is 90.

Contact our Support Team! Please do not hesitate to contact us, we would love to hear fromyou Name:* Company* Email:* Phone:* Industry* Oil & Gas Civil Engineering Environmental Mining Others Message: CAPTCHA Code:*

Vibrating screensare the most important screening machines primarily utilised in the mineral processing industry. They are used to separate slurry feeds containing solid and crushed ores down to approximately 200m in size, and are applicable to both perfectly wetted and dried feed. H-Screening offers high frequency fine screen stakck sizer, linear vibrating screens for fine screening and wet processing, dewatering screens, PLC controlled electromagnetic vibrating fine screen.

Get unmatched customer service and the confidence that comes with owning a Midwestern screening machine. Our sales/service personnel are trained to work with you to optimize your screening process and provide cost-saving screening solutions. Midwestern offers a full line of screening products, including replacement items for most makes and models of screening equipment to save you time and money.

We are proud to offer a FREE full-scale testing center located at Midwesterns headquarters in Massillon, Ohio. Armed with the industrys most advanced testing facility, our customers benefit from the knowledge and experience of some of the best people in the screening industry.

Powder & Bulk Solids International August 24-26, 2021 | Rosemont, IL Donald E. Stephens Convention Center Also known as The Powder Show, its the only event in the western hemisphere bringing together the powder & bulk solids handling and dry processing sector.

MINExpo International September 13-15, 2021 | Las Vegas, NV Las Vegas Convention Center As the worlds largest mining event, the show covers the entire industry exploration, mine development, open pit and underground mining, processing, safety, environmental improvement and more.

PACK EXPO Las Vegas September 27-29, 2021 | Las Vegas, NV Las Vegas Convention Center Bringing together exhibitors and attendees from virtually every vertical market, PACK EXPO Las Vegas is the most comprehensive packaging event in North America.

Widely used in fine wet screening applications, these high frequency screening machines comprise of up to five individual screen decks positioned one above the other operating in parallel. The stacked design allows for high-capacity units in a small footprint. The flow distributor splits the feed stream evenly to the individual polyurethane screen decks (openings down to 45 pm) where feeders distribute the stream across the entire width (up to 6 m) of each screen. Dual vibratory motors provide uniform linear motion to all screen decks. The undersize and oversize streams are individually combined and exit toward the bottom of the Stacked Sizer. Repulp sprays and trays arc an optional addition in between screen sections, which allow for increased screen efficiency.

By classifying by size-only, screens compared to hydrocyclones, give a sharper separation with multi-density feeds (for example, in PbZn operations), and reduce overgrinding of the dense minerals. Operations that replaced hydrocyclones with stackedhigh frequency screening machines in closing ball mill circuits can result in a decrease in the circulating load from 260% to 100% and 10 to 20% increase in circuit throughput.

The high capacity Stacked Sizing/screening machine consists of up to five decks positioned one above the other and all operating in parallel. Its use together with urethane screen surfaces as fine as 75 microns (200 mesh) has made fine wet screening a practical reality in mineral processing operations worldwide. The application of this technology in closed circuit grinding is demonstrated with specific application examples.

Screening is the process of separating particles by size and fine screening typically refers to separations in therange of 10 mm (3/8) to 38 microns (400 mesh). Fine screening, wet or dry, is normally accomplished with highfrequency, low amplitude, vibrating screening machines with either elliptical or straight-line motion. Various types ofwet and dry fine screening machines and the factors affecting their operation have been discussed previously.In fine particle wet screening, the undersize particles arc transported through the screen openings by the fluid andthe fraction of fluid in the slurry will therefore affect the efficiency of the separation. From a practical standpoint, the feed slurry to a fine screen should be around 20% solids by volume to achieve reasonable separation efficiency. Asmost of the fluid passes through the screen openings rather quickly, the fine screening process can be completed in ashort screen length. Therefore screen width, rather than screen area, is an important design consideration for fine wet screening.

Recognition of this concept led to the development of multiple feed point fine wet screening machines, or example, the Multifeed screen consists of three screen panels mounted within a rectangular vibrating frame and is actually three short screens operating in parallel. Each screen panel has its own feed box and the oversize from each panel flows into a common launder and then to the oversize chute. Similarly, the undersize from each of the three panels flows into the undersize hopper. The popular 1.2 m (4 ft) wide by 2.4 m (8 fl) long version has a total effective width of 3.0 m (10 fl) In general, multiple feed point machines have been shown to have 1.5 to 2 times more capacity than a single feed point machine of equivalent size and screen area.

Expanding further on this concept, the Stacked screening machine was introduced in 2001. With a capacity considerably greater than any other type of fine wet screening machine previously available, the Stack Sizer has up to five vibrating screen decks operating in parallel for a total effective width of 5.1 m (17 ft). The decks are positioned one above the other and each deck has its own feed box. A custom-engineered single or multiple-stage flow distribution system is normally included in the scope of supply to representatively split the feed slurry to each Stacked screen and then to the decks on each machine. Ample space is provided between each of the screen decks for clear observation during operation and easy access for maintenance and replacement of screen surfaces. Each screen deck, consisting of two screen panels in series, is equipped with an undersize collection pan which discharges into a common launder with a single outlet. Similarly, the oversize from each of the screen decks collects in a single hopper with a common outlet large vibrating motors rated at 1.9 kW (2 5 HP) each and rotating in opposite directions produce a uniform high frequency linear motion throughout the entire length and width of all screen decks for superior oversize conveyance.

As mentioned above, the fluid passing through the openings carries the undersize particles through the screen openings. The screening process is essentially complete when most of the fluid has passed through the openings. Any remaining undersize particles adhere to the coarse particles and are misdirected to the oversize product An optional repulping system is available for the Stack Sizer in which spray water is directed into a rubber-lined trough located between the two panels on each deck With this feature, oversize from the first panel is reslurried and screened again on the second panel. This repulping action maximizes the correct placement of undersize particles and its use will depend upon the particular objective of the screening machine.

To date, 1000s of screening machines are in operation at mineral processing plants worldwide. Dry mass flow capacity typically ranges from 100 to 350 t/h. This is roughly equivalent to 3 or 4 of the older style Multi-feed screens discussed above Like all screening machines, capacity depends upon many factors such as screen panel opening, weight recovery to oversize, the amount of near-size particles, particle shape, and slurry viscosity.

Sizers are for high capacity in a short compact machine. Generally you can make good cuts or separations with high efficiency. If you need near absolute 99.9% precision cuts, then a sizer cannot do that, and most inclined screeners also cannot. So that is why it is very important to understand what the separation goal is before selecting a screener. You cannot have high capacity and high accuracy + 99.9 in the same machine! This machine does not exist! A sizer generally can accomplish a similar separation of a single inclined screen in 2 or 3 screens, and 1/3 the length. of course a lot depends on the PSD, and how close the remaining particles are on each side of the desired cut.

A high-frequency knocker shaft movement throws the screening material off the screen mesh at a 90 angle - the inclination of the mesh enables the screening material to be transported over the screen. Robust electromagnets operate outside the screen housing, transmitting the vibrations directly into the screen cloth via swinging axes.

A vibrating screen is a machine made with a screening surface vibrated precisely at high speeds. It is utilized particularly for screening mineral, coal, or other fine dry materials. The screening execution is influenced essentially by different factors, for example, hardware limit and point of inclination, in which the performance can estimate by screening effectiveness and flux of the item. While this type of machine is doesnt use for DIY purposes, you may require this for industrial purposes. It is especially essential in the mineral processing industry. If you are considering buying one, check out this article and learn which vibrating screen machine may be perfect for you and your project.

Twofold vibrating engines drive a linear vibrating screen. At the point when the two vibrating engines are turning synchronously, and contrarily, the excitation power creates by the whimsical square counterbalances each other toward the path corresponding to the pivot of the engine. Then, it covers into a resultant power toward the path opposite to the hub of the engine. So, the movement becomes a straight line.

The elliptical vibrating screen is a vibrating screen with an elliptical movement track, which has the upsides of high proficiency, high screening precision, and a wide scope of use. Contrasted with the conventional strainer machine of similar detail, it has a bigger handling limit and higher screening productivity.

A circular vibrating screen is another sort of vibrating screen with a multi-layer screen and high proficiency. As per the kind of materials and the prerequisites of clients, you can use its multiple screening plates. it were introduced in the seat type. The alteration of the screen surface edge can acknowledge by changing the position and tallness of the spring support. This screen is used for mining, building materials, transportation, energy, chemical industry.

The working surface of the roller screen is made out of a progression of moving shafts that masterminded on a level plane, on which there are many screen plates. When working, the fine material goes through the hole between the roller or the screen plate. In this way, enormous squares of materials are driven by rollers, moving to the closures and releasing from the outlets. Roller screens are usually widely used in the conventional coal industry.

High frequency vibrating screen is likewise called a high-frequency screen for short. High frequency vibrating screen is made out of exciter, screen outline, supporting, suspension spring and screen, and so on. This type of vibrating screen is the most significant screening machine in the mineral preparing industry, which is reasonable for totally wet or dry crude materials.

Rotary vibrating screen principally utilize for the grouping of materials with high screening effectiveness and fine screening precision. It features a completely shut structure, no flying powder, no spillage of fluid, no obstructing of work, programmed release, no material stockpiling in the machine, no dead point of matrix structure, expanded screen territory, etc. Any molecule, powder, and bodily fluid can screen inside its specific range. The machine usually used for characterization, arrangement, and filtration in nourishment, substance, metal, mining, and some other ventures.

Horizontal screen has the benefits of both slanted screen and straight vibrating screen. The machine has the highlights of good screen penetrability, enormous handling limit, and small installed height. The establishment point of the regular vibrating screen is 15-30, while the establishment of a flat screen is corresponding to the ground, or somewhat slanted 0-5.

Heavy inclined screen can apply to the treatment of debris from the quarry, mine, and building destruction. It can also utilize in the treatment of topsoil, the reusing of development materials, the screening of rock, the screening of gravel and aggregates, etc.

Grizzly screen regularly utilizes for pre-screening before coarse and medium pulverizing of materials. The work size is by and large>50mm, yet some of the time <25mm. This machines productivity is low, but screen efficiency is not that high. Also, quite often, the mesh tends to get a block.

The banana screen has a screen plate with various areas and diverse plunge edges. The longitudinal segment is a broken line, while the entire screen resembles a banana shape. The banana screen is, for the most part, appropriate for the arrangement of huge and medium-sized materials with high substance of fine particles. It can likewise utilize for drying out and demoralization.

While you picking vibrating screens, the material qualities should consider, including the substance of material particles under the screen, the substance of troublesome screen particles, material dampness, the shape and explicit gravity of the material, and the substance of clay. Professional vibrating screens makers could give serious vibrating screen value, assorted variety redid vibrating screen models, auspicious after-deals administration, save parts, and can keep on offering types of assistance for clients entire creation circle.

More You May Like

Copyright © 2021 Indext Machinery All rights reserved sitemap Privacy Policy