More about the Cloud

Apparently, what we are doing is being described with a fashionable buzzword - Cloud Computing. As it often happens with buzzwords, this one is being used to describe anything that is not really understood. Well, I just happened to come across this fantastic video about Cloud Computing, where it is explained in simple terms.

Applying this to Condition Monitoring means that a virtualized application like InSite can run on any server and hence exists in the Cloud. The SaaS model lets users pay for what they use, per data channels they monitor. As a result, the monitoring service becomes a utility, which makes it so easy to implement and use. No headaches related to hardware-software issues, and easy outsourcing options for additional services.

And we shouldn't forget why we are doing this - Predictive Maintenance, right? It's knowing when the machines need maintenance, only before they break down and stop.

Cloud Computing in Action

If you make the technology right, people will pick it up and embrace it sooner than you can imagine. In his video tutorial Tom Hoenig of GTI uses the InSite system to demonstrate remote condition monitoring at a plant in Rochester, NY area. What Tom does not mention is that he is monitoring that machine from his office in Manchester, NH and the application server that stores the data, does all the calculations, and delivers the user interface to his browser is running in a data center in Chicago and is maintained by InCheck. And yet, the application feels seamless as if everything is running on Tom's iBook.

There is more in this then just the convenience of access to the data from anywhere. For starters, there is no software to install as the monitoring application runs directly in a standard web browser. There is no system maintenance for the plant operators as they only have to install the sensors and wireless data acquisition modules. And the most important aspect of all is that this system reduces costs because the users do not have to own and operate the server's complex software and hardware. Instead, they get it all as a service.

Machine Diagnostics

It shouldn't come as a surprise that periodic forces in rotating machines appear to be related to the rotating speed. Most of these forces produce excitation at frequencies proportional to the operating frequency of a machine. Machine structure transmits the forces to the foundation. Depending on mass-elastic properties of the machine components the machine structure produces response to the excitation forces. This response is what we measure as vibration. In other words, when operating, stationary machine components move periodically (vibrate) under internal periodic forces. This movement, i.e. vibration, can be measured and analysed.

The excitation forces in rotating machines can be related to the machine function, such as blade pass excitation in turbo machines or to mechanical sources that are present in every machine. For example, there is always residual imbalance in the rotor, which produces force at rotating frequency. If there is coupling misalignment, it will produce forces at 2x, 3x, or other integral multiples of the rotating speed, depending on the design.

When components start wearing out or become defective due to contamination or abuse, the forcing frequencies change. These changes can be picked up by vibration analysis software or a vibration analyst. Often the source of the problem or at least a component that has developed a fault can be determined just by analyzing the vibration data. Every machine component has its vibration signature, which changes when a fault develops.

This is why dynamic vibration analysis is so important. Since the vibration spectrum shows vibration levels vs. frequency, it is the first tool that is often used.

FFT spectrum tips

It is almost a given today to analyze vibration using data in frequency domain or as a spectrum. Engineers rely so much on the spectrum analysis that it is easy to forget that there are accuracy trade offs that are built into the process of obtaining the spectrum. Windowing is one of them. It is built in almost all vibration analyzers and

FFT is based on the Fourier theorem, which assumes that we deal with an infinitely long time function. Since we don't have infinite time, we just take a snapshot of time data and make the algorithm think that this piece of data repeats infinitely. Since repeating the time data sample causes abrupt changes at the ends of the sample, the process causes a truncation error that is also called spectral leakage. A standard technique to deal with this problem is to use a Window filter. A variety of functions can be used, which brings the values at the beginning and the end of a time data sample to zero or close to zero. This solves the spectral leakage problem, however all window functions reduce the amount of useful data used in the FFT calculation and distort the spectrum, often causing the peaks in a spectrum appear wider and lower. If a vibration analyzer has a choice of window functions it is important to compare spectra processed with the same window function all the time.

If the same analyzer is used for vibration measurement and for modal analysis, the window functions have to be different. For example, for vibration measurements Hann (also called Hanning) window is often used, while for modal analysis a rectangular window (no window function) may be preferred. It is important that the window functions are set correctly each time the analyzer is used for a new task.

Vibration Analyzer Settings Discussion

I just read this article by Jason Tranter about vibration measurement errors. The topic is very close to what we have been talking about. It is interesting and the advice is very good, however tips like these are rarely followed because so much of this is just arbitrary. The author is not explaining why his recommendations make sense. For example, the requirement to select "Fmax of 70 times the running speed of the shaft". Why? Why not 50 or 100? By the way, in the example that follows the author uses Fmax at 100 times the speed (1800 RPM is 30Hz and he is using Fmax 3000 Hz).

Another arbitrary requirement is that the shaft "should turn at least 50 times during your measurement". Again, why? There must be a reason to it and a vibration analyst will be able to apply this rule more efficiently if he or she knows why the rule exists. And, while we are at it, why not give a simple formula that would help to calculate the number of shaft revolutions during the measurement? The author skips this part. I would use this formula:

revolutions = (lines/Fmax)*((1-overlap/100%)*(averages-1)+1)*RPM/60

Following the example from the article, where lines=3200, Fmax=3000, overlap=67%, averages=5, and RPM=1800, this formula yields 74.24 revolutions. Well, the 75 revolutions sited in the article is close enough.

In vibration analysis, as in many other areas, there is no single answer to how the techniques should be applied. It is true that vibration analyzers today have all these capabilities but using them by following a set of arbitrary recommendations may not work in all situations. It is important to understand the objectives of the measurement and know the machine being tested and its operating characteristics.

There are trade offs in using settings that are not dictated by the task at hand. For example, if unnecessarily long samples are taken, there is a chance that a fluctuation in rotating speed will smear the spectrum peaks at forcing frequencies. If the analysis bandwidth (Fmax) is set too high, there may not be enough resolution to analyze the frequencies of interest. If the resolution is set too high, it might lead to a longer measurement and large file size.

Instrumentation - Dynamic Resoluiton

When sensor signals are being converted to data the capabilities of ADC (analog to digital converter) should be considered. The dynamic resolution of an ADC is related to a number of steps the original signal is approximated with. If the ADC has 10 bit resolution, that means that the full input range will be digitized using 2^10 (two to the power of ten) or 1024 steps. For many applications it is too crude, especially if the signal is not amplified. Typically, for vibration measurements ADCs with 16 or even 24 bits of resolution are used. For industrial vibration monitoring a 16 bit ADC is quite enough. A sensor with the sensitivity of 100 mV/g and the signal range of -10/+10V will have the smallest step of 3mg at 16 bit ADC. Dynamic resolution is sometimes listed in dB in device specifications.

If more dynamic resolution is needed, the signal can be amplified, or an ADC with higher resolution can be used. There are trade-offs with either method. An analog amplifier can introduce noise while a higher resolution ADC results in a more expensive device. There is a signal processing technique that can boost dynamic resolution through oversampling.

Instrumentation: Data Acquisition: Sampling Rate

When a dynamic process such as vibration has to be studied it is important to perform measurements correctly. The data acquisition process converts an analog signal obtained from a sensor into a discrete function. This is done by sampling the signal at equal intervals. The speed at which the sampling takes place is called sampling rate. Why do it? Because the resulting data is very convenient to transmit, process and store.

The faster the sampling rate, the closer the digital function is to the original signal. So, what sampling rate is really needed? It depends on the highest frequency of interest. Technically, the sampling rate has to be at least twice the highest frequency of interest. The ratio used in the industry is 2.56, which is called Nyquist factor. This means that if the highest frequency we would like to study is 1000 Hz, the sampling rate has to be at least 2560 samples per second.

Now, how much data do we have to collect to have enough to calculate a spectrum? This depends on the spectrum resolution we are trying to achieve. At 800 lines per spectrum we will need 800*2.56=2048 samples. Here, again we multiply by the same Nyquist factor. Time needed to collect the samples we need is found by dividing the number of samples by the sampling rate: 2048/2560=0.8 seconds. It is easy to see that at lower frequencies and higher spectrum resolution it might take quite a while to collect the data. That is why it is sometimes takes so long to acquire a data set.