A modified cepstrum analysis applied to vibrational signals

This paper investigates the application of better signal processing techniques to improve the signal to noise ratio in vibrational signals used for condition monitoring. Environmental conditions such as instantaneous speed variations as well as the presence of multiple fault conditions can however obscure the defect signals that are required for reliable diagnostics and can lead to faulty diagnostic decisions. While these problems can be solved with the right combination of techniques, the difficulty of obtaining sufficiently large measured data sets on which to train these techniques remain. Artificially generated training data sets, by empirical modeling of defects, is hence investigated and a simple vibrational model, which includes the effect of period variation, is proposed for the bearing defect data set by Hoffman and van der Merwe (see Proceedings of the 5th WSES International Conference on Circuits, Systems, Communications and Computers (CSCC 2001), Rethymno, Greece, July 2001, p.209-214). Signal processing techniques, such as the cepstrum, can be influenced by the noise caused by instantaneous angular speed variations of the shaft. A novel modified cepstrum analysis is proposed which is less sensitive to small Fourier components encountered in a simulated vibration signal.