The dreaded time domain plot - a powerful tool, but a number of questions for the analyst:
This section of the manual will provide you with information on how to use and interpret the time domain plot. The time domain, of course, is where the reading begins - an analogue measurement of how the surface is moving. This analogue signal is fed from the transducer to the analyser where it is converted to a digital signal - it goes through an A/D converter. The result of this process can be seen above in the plot above.
It is important to realize that it is experience (i.e. practice) that creates a 'comfort' level for the analyst in interpreting the time domain plot. Experience in setting it up properly and experience in being able to recognize what you are seeing - the pattern of what you are seeing. Looking at Figure 1, what do you see ? A fuzzy caterpillar (from about 1 inch away) - looks like a rough winter is coming. But let's get closer and see what else we can see.
Let's go back to that nice clean sine wave you see all the time - at least all the time you are sitting in a training class. That is what you see in Figure 1. Performing an FFT on Figure 1 would generate the plot you see in Figure 2 - a single peak at 1x rpm.
The FFT is created with a peak at 1500 cpm or 25 Hz. The amplitude shown will be based on the Window type shown and whether you have a signal detection of RMS, peak, peak to peak or true-peak.
Unfortunately, in the mechanical world there are only two problems that cause such a pure sinusoid to occur (and it will only be pure if they are the only problems present):
Time Domain Plots and Frequency Modulation.
But could there be another explanation for the signal shape seen on those pages ? Let's return to our discussion of the actual, real-life vibration signal we looked at a few pages back. We discussed how there can be some variation in the free rotation of the shaft - a momentary 'binding' action that can occur for a number of reasons. In that situation, we considered the possibility of the gears being improperly set. That would create more resistance to rotation when the teeth were bottomed out than opposite that point. It would momentarily slow down the rotation.
Let's examine the 'frequency modulated' signal shown in Figure 1:
This is frequency modulation. What is happening here may or may not be evident if we were to analyse the time domain signal. But remember, the question we are discussing here is: How will the FFT treat this phenomenon?
We have discussed frequency modulation and its impact on the spectrum plot - namely, it creates harmonics. But we have also touched on amplitude modulation - now let's cover it more in-depth. Amplitude modulation is a increase and decrease in the amplitude of a particular frequency at a different frequency. That's simple enough, right? But what effect does it have on the FFT?
Let's look at some examples. What do you make of the time domain plot taken on a gearbox shown here:
This is a case of a low frequency cycle (occurring 15 times over the time sample) and a high frequency (occurring many times for each of the low frequency cycles). This is known as 'a high frequency riding a low frequency'. For analysis, let's zoom in:
This shows only about one of the low frequency cycles. The high frequency could be a gear meshing frequency. The low frequency is at 1x rpm. How many teeth are on the gear ? This is another advantage of using time domain on gearboxes - you can actually obtain detailed internal information that you can only guess at on the spectrum. Count the small peaks from the top of one low frequency peak to the next. There are 23 teeth. What does the spectrum look like?
Both the amplitudes and the frequencies are constant - there is no modulation in either - so you only get the peaks that are actually being generated. Now let's consider some variations on this "perfect" gearbox.
Obviously, time domain is a powerful tool. But what kinds of problems and situations are better analyzed with the time domain plot?
Additionally, you can gather information related to the machine condition such as: