There are many various signal processing techniques implemented for the vibration diagnosis of bearings on rotating machines. This short article from Mukesh Shah provides an introduction to three main categories of signal processing techniques: Frequency methods, Time methods or Statistical methods, and Filtering methods applied to time signals.

There are many various signal processing techniques implemented for the vibration diagnosis of bearings on rotating machines. They can be sorted into three main categories:

- Frequency methods: Narrow-band spectral analysis (FFT), envelope analysis (amplitude demodulation), cepstrum analysis.

- Time methods or statistical methods: RMS value (overall level over a given frequency band, usually for acceleration and in high frequencies for bearings: >1 kHz), peak value, peak factor, kurtosis and manufacturer’s acceptance tests: defect factor DEF, SEE technology, Gse, etc.

- Filtering methods applied to time signals: essentially high pass and band pass, denoising by spectral subtraction of all periodic components. Frequency processing consists in detecting the presence of periodicities due to repeated shocks generated by the possible marking of an inner or outer raceway or by the scaling of rolling elements.

Among the aforementioned techniques, envelope analysis is the most advanced tool.

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