Feature-level fusion based on wavelet transform and artificial neural network for fault diagnosis of planetary gearbox using acoustic and vibration signals

In this article, an intelligent system based on an artificial neural networks (ANN) classifier is proposed for fault diagnosis and classification of planetary gearboxes based on fusing acoustic and vibration data at the feature level. First, the acoustic and vibration signals of the planetary gearbox were collected simultaneously in four gearbox conditions: (1) healthy; (2) worn tooth on planet gear; (3) cracked tooth on ring gear; and (4) broken tooth on ring gear. Then, the time domain signals were transformed to the time-frequency domain by wavelet transform. Thirty statistical features were then extracted from each signal and used as feature vectors to an ANN classifier. The primary classification of the faults was undertaken based on the extracted features from each sensor. The classification accuracy of acoustic and vibration data was about 88.4% and 86.9%, respectively. The final classification accuracy using fused features was 98.6%, indicating the superiority of the proposed method for fault diagnosis of a planetary gearbox. The 10% accuracy increase gained through using the data fusion method can significantly enhance the quality and accuracy of fault diagnosis and, as a result, condition monitoring of the machinery.