Static vs. Dynamic data acquisition

I have been asked many times to define Static and Dynamic data. In short, the Static data is sufficiently represented just by one sample. Dynamic data is more complex, and is represented by a data set. For example, if we have a sensor that measures temperature, we can get a sample at any time and each time we get a value that represents the temperature at that moment. However, if we sample a sound wave at any time, we will get a random value that is not representative of the sound intensity or frequency.

Data acquisition is the process of converting an analog signal to data by sampling the signal at certain time intervals. The signal is usually an output of a sensor and represents a physical parameter being measured by the sensor. Depending on the type of the sensor, the data can be sampled at a faster or slower rate providing more or less detailed information. A signal from a temperature sensor, for example, changes slowly and can be sampled at a relatively slow rate without loss of quality. The temperature signal is called static as it represents its corresponding value at any time.

A signal from an accelerometer represents a movement of the machine housing on which the accelerometer is installed. This signal contains a sum of vibrations at different frequencies that are generated in different parts of the machine. Analyzing magnitudes of these vibrations helps detect changes in the machine due to wear, lack of lubrication, imbalance, misalignment, and other mechanical, electrical, or process abnormalities. Because of its dynamic nature the accelerometer signal can’t be sampled at an arbitrary time to yield a meaningful result. The data has to be acquired many times in a rapid succession to obtain a data set.

To obtain a proper set of data for analysis the vibration signal has to be sampled at a relatively high sampling rate. The sampling rate defines the analysis bandwidth, i.e. the highest frequency component that is available for analysis. The length of the data set defines the frequency resolution, i.e. the lowest difference between two frequencies that can be detected. The data set then represents a function of vibration magnitude over time. Using further data processing the software can present the data in many other forms, including a spectrum (magnitude vs. frequency) and calculate various overall and statistical parameters.

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.

Instrumentation -- Sensors

Since we are talking specifically about machine condition monitoring, our primary interest will be in vibration sensors. Over many years the standard vibration sensor has been an accelerometer. The sensing technology in a standard accelerometer is pretty much the same today as it was thirty years ago.

In spite of its wide use the standard accelerometer is not an ideal vibration sensor. It requires a charge amplifier that is usually built into the sensor body and an analog signal conditioner, which adds to the cost. Low frequency applications are challenging because the sensor is not sensitive to static acceleration while the dynamic acceleration is very low at low frequencies. These sensors require regular recalibration in condition monitoring applications. However the main issue with traditional accelerometers is cost. An accurate, temperature stable accelerometer with a wide frequency range can cost a few hundred dollars.

The good news is that soon this is going to change. New technology is already here and is waiting to be widely adopted. I am talking about MEMS accelerometers. The MEMS technology has been widely used for sensors and MEMS accelerometers are commonly used in electronic devices as motion sensors. Industrial applications could not benefit from the MEMS technology until last year when Analog Devices has released the ADXL001 accelerometer. Designed specifically for industrial applications, this accelerometer features a wide frequency range, high temperature stability, excellent low frequency characteristics, and does not need recalibration.

Sounds like a perfect vibration sensor? Well, not so fast. There is a gap between the sensor's microchip form factor and the industry's requirement for robustness. This sensor needs an appropriate packaging, in which the sensor will be sealed from the environment and have means to be mounted to a machine. InCheck Technologies is working on a version that a user will be able to permanently mount into a small hole in a bearing housing. The package will also include a temperature sensor microchip, forming a dual sensor.

Speaking about temperature sensors, MEMS sensors can compete with traditional thermocouples and RTD's in the -40°C to +125°C temperature range. Unlike RTD's MEMS temperature sensors produce linear output and unlike thermocouples they do not require external electronics.

Did I mention the main advantage of the MEMS sensors? Yes, they are pretty inexpensive. The ADXL001 is still a little pricey, but it already beats the traditional technology hands down. And since the MEMS output is compatible with standard ADC input, no addition analog electronic components are necessary. With machine monitoring applications the low cost will be the key to wide adoption. The future apparently is with the MEMS technology.

Machine Health Monitoring

Assuming now that we are all on board for predictive maintenance, what do we do next? Our goal is to prevent failures, so, obviously, we need to monitor our machines. This could be done just by inspection (listening to noise, touch for temperature and vibration, visual inspection) or by collecting data. Since the inspection by perception is biased and hard to document, I personally prefer data. Specifically, if we are talking about rotating machines, we need vibration data. With proper processing the vibration data can help diagnose almost any mechanical problem, especially when the data is tracked over time. Other parameters can be monitored as necessary. Temperature, pressure, electrical parameters, etc. - all can yield valuable machine health data.

We will talk about manual "walk around" data collection on a separate occasion. For now we assume a permanent installation. Whether we collect data manually or install permanent sensors, the monitoring system has to have a few layers between Machines and Users that look like this:

Machines --> Instrumentation --> Communication --> Processing and Storage --> Presentation --> Users

Instrumentation layer consists of sensors and data acquisition hardware. To minimize cost of installation the data acquisition hardware should be installed close to the machines. Long sensor wires are not only expensive and hard to install, they sometimes cause deterioration of the analog signal and loss of accuracy. The instrumentation layer outputs data in digital format.

Communication layer transmits the data to a server where it is processed and stored. Today it would be a shame if we ignored standard network infrastructure that makes this transmission easy. Costs are drastically reduced by using standard off the shelf networking components. Standard Ethernet (wired or wireless) and the Internet are the best choice for the communication layer.

Processing and Storage layer is typically a dedicated server computer that runs a few key pieces of software - communication server, digital signal processor, and database. It also contains an alert generator that informs users if something goes wrong.

Presentation layer is a web portal where user can login to monitor the data and control the system. The presentation layer sends to user's browser a program that shows the data in graphic format, gives interactive access to stored data and to all machines regardless of their location. The web portal can be accessed from anywhere with a secure login.

Clearly, the above system architecture is somewhat simplified, but for the purposes of this discussion it is fine. The point we are trying to make here is that today's technology provides tools to make a very inexpensive system that can monitor machine health condition with high accuracy.

Next time we will talk about sensors.

Welcome to InCheck Technologies blog!

Welcome!

In this blog we will be discussing various aspects of remote machine monitoring technology for predictive maintenance. This topic is very important in the industry. Remote monitoring helps machine users to organize machine maintenance in a proactive way, saving money in the process.

Anybody who uses industrial machines knows that they have to be maintained. What people sometimes don't realize is how much this maintenance costs over the life of a machine. So, let's take a look.

Each machine provides a specific service. When you are buying a pump, your goal is to obtain pumping service from it. The cost of pumping service however is not limited to the cost of the pump but has to include installation, energy cost, cost of maintenance, and, at the end of our pump's life, the cost of disposal. Surprisingly, the cost of maintenance is second only to energy costs and can be up to 35% of the total, leaving the initial cost (the cost of the pump and installation) behind at about 10 % of the total, see the diagram below.

Typical life cycle costs of an industrial pump (source: Hydraulic Institute)

Unfortunately, this is just the cost of regular maintenance activities. It does not include the cost of downtime or additional costs that are usually incurred if a machine fails suddenly. This is a real problem. Due to unexpected failures this cost can be dramatic. In many cases the service has to be restored as soon as possible, leaving little choice but paying extra for parts, labor, overtimes, and lost production capability. And we are not even talking about the cost of poor maintenance.

The answer to the challenge lies in a properly organized predictive maintenance program. The goal of such a program is to monitor health condition of each machine, track developing faults, and repair the machines before they fail. This is easier to say than to do. There are two parts in this endeavor - organizational and technological. We will be talking mostly of the technology part, occasionally touching on how to make it work from the organizational standpoint.