Classification of faults in gearboxes - Pre-processing algorithms and neural networks

Classical signal processing techniques when combined with pattern classification analysis can provide an automated fault detection procedure for machinery diagnostics. Artificial neural networks have recently been established as a powerful method of pattern recognition. The neural network-based fault detection approach usually requires pre-processing algorithms which enhance the fault features, reducing their number at the same time. Various time-invariant and time-variant signal pre-processing algorithms are studied here. These include spectral analysis, time domain averaging, envelope detection, Wigner-Ville distributions and wavelet transforms. A neural network pattern classifier with pre-processing algorithms is applied to experimental data in the form of vibration records taken from a controlled tooth fault in a pair of meshing spur gears. The results show that faults can be detected and classified without errors.