Detection and Advancement Monitoring of Distributed Pitting Failure in Gears

Conventional methods (i.e. time, frequency and cepstrum) can routinely be used to reveal fault-indicating information in the vibration signal. In recent years, Wavelet analysis, which can lead to the clear identification of the nature of faults, are widely used to describe machine condition. Capability of this method in the detection of any abnormality can be further improved when its low-order frequency moments are considered. This paper presents the use of vibration-based techniques in the early detection and advancement monitoring of distributed pitting fault. The pits were seeded on all of the gear tooth surfaces in differing degrees of severity, and intended to replicate the pitting damage due to misalignment. With each fault severity, the helical gears were tested and the resulting vibration data were recorded. The application of employed vibration-based methods (i.e. time, frequency, cepstrum, and wavelet transform: scalogram and its mean frequency variation) to each set of experimental data are presented. It has been found that presence of pitting fault cannot be clearly revealed by the conventional unless fault severity is significantly large. In contrast, the scalogram and especially its mean frequency variation provide early indications of presence and progression of pitting faults in gears even when the fault severity is considerably smaller.