FFT spectrum analysis
Among methods for viewing vibration and noise signals and pinpointing the causes, a Fast Fourier Transform (FFT) spectrum is perhaps the most useful. Vibration and noise signals are broken down into specific amplitudes at various frequencies. Because each equipment component vibrates at a certain individual rate, maintenance personnel by processing these signals can distinguish between several different rates and then determine which rate coincides with which component. The resulting FFT spectrum can point the way to the location, cause and stage of a problem.
Figure 2. Portable unit stores and analyzes data which also can be uploaded to a computer for more detailed analysis.
User-friendly FFT analyzers have been developed to measure vibration and noise signals and separate them into their component frequencies. These tools can display spectrum information in simplified formats to enable a first-pass diagnosis of machinery condition or identify areas for further scrutiny (Figure 3). The placement of FFT analyzer sensors, setup, and the process of taking measurements can be performed without taking machines out of service.
Time domain analysis
Time domain (or time waveform) signals offer one of the few methods to detect certain types of problems. Time domain analysis also can bolster confidence that data in the frequency spectrum have been properly interpreted; in some instances, it can help confirm a particular problem that simply may have been a “best guess” scenario.
Time domain is the actual data received from machinery and further processed through Fourier Transform to arrive at the frequency domain. This allows personnel to discern actual frequencies and amplitudes of components within a machine and helps target components that may be failing or faulty processes that could have gone undetected until machinery failure.
Figure 3. Particular peaks relate to specific equipment components, enabling pinpointing of causes of vibration and noise.
In general, the time domain is a record of events as they happen and is very similar to looking at recorded sound. A sine wave produced by a signal generator in the lab would appear in the time domain spectrum just as it does on the screen of an oscilloscope. In the real world, though, complications arise because a machine does not produce a solitary signal. That’s where a time domain signal shines.
For example, an operating motor connected to a gearbox and then to a compressor produces thousands or millions of signals that add and subtract to and from each other based upon their relationships and the influence of external forces. All ultimately can be separated and discerned from a time domain signal.
The need for time domain data is absolutely mandatory for some applications. These include cracked, broken or deformed gear teeth in gearboxes; rolling bearing defects on very-low-speed (less than 10 rpm) machines; motor startup transient issues resulting in bearing deterioration and winding problems; and, for reciprocating compressors, short-lived impact-type vibration concerns, such as piston slap, main bearings and inlet or discharge valve problems.
One of the generally under-appreciated CM techniques involves a bump (or rap) test. It can provide operators with a quick indication of whether high levels of vibration or noise are due to the dynamic or static parts of a system. This impact test is carried out to excite the structure to allow measurement of natural frequencies, which then can determine whether high vibration or noise levels are due to resonance or a potential problem with the machinery.