Diagnostic Techniques-Part 2 Frequency Domain
Last Updated: 06/24/2017
Frequency domain measurements and analysis have
become increasingly popular to diagnose a particular machine fault. This measurement mode
relies on processing the transducer output signal using Fast Fourier Transform (FFT)
algorithms to display the signal amplitudes as a function of frequency. FFT processing
essentially separates complex signals into individual components having a single frequency
content. This type of display is commonly termed a spectrum.
An enhancement of spectral analysis is to define specific frequency ranges to perform band analysis. Conceptually, band analysis is similar to filtering a signal. The "filter" searches for frequencies only within its frequency range. Certain permanently installed machine monitoring systems offer this capability. This feature is quite effective, once the particular spectral range and resolution has been determined, to rapidly diagnose machine faults.
A band is essentially a band pass filter allowing only the frequencies with the selected range to be measured. All other frequencies are excluded from analysis. Many modern machine monitor systems are capable of monitoring specific frequency ranges using band analysis.
Each line of resolution can be viewed as a bucket or pail of a specific size. The signal frequencies can be viewed as a tennis ball. If a tennis ball's frequency matches the frequency range of the bucket, it is placed in the bucket. As the bucket fills with tennis balls the peaks on the spectrum display rise. Should the frequency range of the buckets be too large, the tennis balls will not be adequately separated to detect individual frequencies. This would lead to always using the highest resolution for spectral or band analysis. Higher resolutions require greater amounts of time to display the spectrum, thus a balance must be reached between the capture time and the spectral resolution.
Frequency Domain Checklist