Crack Fault Detection for a Gearbox Using Discrete Wavelet Transform and an Adaptive Resonance Theory Neural Network

In this paper, a new approach using discrete wavelet transform and an adaptive resonance theory neural network for crack fault detection of a gearbox is proposed.With the use of a multi-resolution analytical property of the discrete wavelet transform, the signals are decomposed into a series of sub-bands.The changes of sub-band energy are thought to be caused by the crack fault.Therefore, the relative wavelet energy is proposed as a feature.An artificial neural network is introduced for the detection of crack faults.Due to differences in operating environments, it is difficult to acquire typical, known samples of such faults.An adaptive resonance theory neural network is proposed in order to recognize the changing trend of crack faults without known samples on the basis of extracting the relative wavelet energy as an input eigenvector.The proposed method is applied to the vibration signals collected from a gearbox to diagnose a gear crack fault.The results show that the relative wavelet energy can effectively extract the signal feature and that the adaptive resonance theory neural network can recognize the changing trend from the normal state to a crack fault before the occurrence of a broken tooth fault.