Abstract
An experimental evaluation of the ability of sound pressure microphones to diagnose different machinery conditions in noisy environments was performed. An adaptive filtering (ANC) routine was incorporated to reduce the noise. The detection process utilized frequency spectra of the data, along with cepstrum and kurtosis methods of analysis. Two different machine components were monitored: ball bearings in a ball bearings test stand and milling bits in a milling machine. The effect of the placement of the microphones on the ANC routine to reduce the background noise in the signal was investigated and found to influence the results. The results show that the sound pressure microphones could not reliably diagnose ball bearing condition but could diagnose the milling machine bit condition.
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