Sometimes data has spikes which are clearly artefacts of the processing or are due to some other external source. One is used to seeing these on time series but in some cases there are unrepresentative “spikes” in the frequency analysed data. Here we discuss how we can use spectrum smoothing to alleviate the problem. An example spectrum is shown below.
The measurement of torsional twist, or the twist angle, between two points along a shaft or through a gear train may be derived from a pair of tacho signals, one at each end of the shaft. Typically the tacho signals would be derived from gear teeth giving a known number of pulses/revolution. For example one end of a shaft could have a gear wheel with say 60 teeth giving 60 pulses/revolutions when measured with say an inductive or eddy current probe. (more…)
By default the RedHat 7.2 installation uses the GNOME Display Manager GDM or possibly the KDE Display Manager KDM. Both these Display Managers are very powerful and allow users to select the type of session to start ( such as GNOME or KDE ). There are also options and menus to shutdown or reboot the system. For a PROTOR installation where we want to control the users session then using the basic XFree86 display manager XDM is preferable.
In some configuration it may be required or desirable for Linux and Window NT to co-exist on the same system. Presuming Windows NT has been initially installed then the system will boot through the Windows NT Boot Loader. Assuming that a Linux system has been added to a spare partition or additional disk then the requirement is to add an option to the NT Boot Loader such that either Windows NT or Linux may be booted.
In this article, we look at the relationships between frequency, the unit Hertz and order tracking. The most common form of digitising data is to use a regular time-based method. Data is sampled at a constant rate specified as a number of samples/second. The Nyquist frequency, fN, is defined such that fN = SampleRate/2. As discussed elsewhere, Shannon’s Sampling Theorem tells us that if the signal we are sampling is band limited so that all the information is at frequencies less than fN then we are alias free and have a valid digitised signal. Furthermore, the theorem assures us that we have all the available information on the signal.
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Accurate measurement of a signal depends on the dynamic range and the overall level of the data acquisition system. The overall level setting may be thought of as determining the largest signal that can be measured. This clearly depends on the present gain setting. That is the overall level is related to the gain. Clearly if the overall level is too small (gain too high) then the signal will be clipped and we will have poor quality data. The dynamic range then tells us that for the given overall level what is the smallest signal we can measure accurately whilst simultaneously measuring the large signal.
In a very simple sense suppose we have an artificial signal which consists of a sinewave at a large amplitude A for the first half and that this is followed by a sinewave with a small amplitude a for the second half. We will set the gain (the overall level) to allow the best measurement of the A sinewave. The dynamic range tells us how small a may be so we can also measure that without changing settings.
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It is sometimes necessary to perform high pass filtering to eliminate low frequency signals. These may arise for instance from whole body vibrations when perhaps our interest is in higher frequency components from a substructure such as an engine or gearbox mounting. The vibration levels are speed sensitive and the usual scheme is to record a once per revolution ‘tacho’ signal with the vibration data. The tacho signal, which ideally is a nice regular pulse train, is processed to find rotational speed and hence to select which part of the vibration signal is to be frequency analyzed. The most common form of analysis is a waterfall type such as shown below.
In many real-world applications it is impossible to avoid “spikes” or “dropouts” in data that we record. Many people assume that these only cause problems with their data if they become obvious. This is not always the case. Consider the following two time histories.
Not all systems vary linearly. One very well-known case is, of course, thermocouples. International standard curves are available for these, so they present little difficulty. The issue discussed here is determining a non-linear calibration curve and, if appropriate, reducing it to a polynomial.
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Some devices, particularly digital tape recorders, apply A-weighting to all their data to achieve acceptable data compression. This is fine unless you want to analyse the unweighted data or apply a different weighting factor. Using Prosig’s DATS software, it is a simple task to instruct the WEIGHT module to either unweight the data or remove one weighting factor and apply another.
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To illustrate the use of the cross correlation function, a source location example is shown below. For this, it is assumed that there is a noise source at some unknown position between 2 microphones. A cross correlation technique and a transfer function like approach were used to determine the location. (more…)
Sometimes we have digitised data at a much higher rate than we need. How can we downsample data? If I wanted to say halve the sample rate can I just throw away every other data point?
The answer is NO, except in pathological conditions where you know that there is no frequency content above the new Nyquist frequency.
The PROB module in DATS for Windows provides, amongst other options, a probability density analysis. Also, the signal generation suite has a module, GENPRB, which generates a classical gaussian probability density curve (and others). How then may these be used to compare the probability density of our measured signal with that of a true Gaussian one. The method is quite straight forward and is a matter of scaling.
In many instances we need to filter a signal to remove unwanted frequencies. If we use classical filters such as Butterworth, Chebyshev or even Bessel then a phase delay is introduced. This phase delay is itself a function of frequency so that the signal content at one frequency is delayed a different amount to that at another frequency. Why does this matter?
For various reasons data captured in the real world often contains spikes that will give erroneous results when analysed. The DATS software package provides various ways of editing and to remove spikes from data. Let us consider a real life case history.
A common requirement to measure overall level vibration (or noise) as a function of time. Now, the overall level is a measure of the total dynamic energy in the signal. That is it does not contain the energy due to the DC level, which is the same as the mean value. The overall level is often loosely referred to as the signal RMS value. However the formal definition of the RMS level is that it contains the DC level as well as the dynamic energy level. If only the dynamic contribution is required then the measure needed is, strictly speaking, the Standard Deviation (SD). Sometimes it is useful to refer to the SD as the Dynamic RMS.
The Articulation Index or AI gives a measure of the intelligibility of hearing speech in a given noise environment. The metric was originally developed in 1949 in order to give a single value that categorised the speech intelligibility of a communication system. The basic interpretation of the AI value is the higher the value then the easier it is to hear the spoken word. The AI value is expressed either as a factor in the range zero to unity or as a percentage.
This note describes the importance and setup of a keyphasor or tachometer signal for PROTOR Remote Monitoring Data Acquisition System (RMDAS) units. RMDAS units are provided with manual tachometer conditioning…
It is possible to produce an shaft gap display within PROTOR for two perpendicularly mounted shaft proximity probes for system where the DC component of the proximity signal is measured...…
