Gear fault diagnosis based on bidimensional time-frequency information theoretic features and error-correcting output codes: A multi-class support vector machine

Abstract

Fault diagnosis of gears plays an important role in reducing downtime and maximizing efficiency of rotating machinery. Vibration is popular parameter for gear fault detection. The occurrence of faults produces recurring transient impulses in the gear vibration signals. However, these transient features are heavily masked by background noises making it difficult to investigate gear faults. Furthermore, the development of automated fault diagnosis techniques requiring minimal human supervision poses another big challenge. Consequently, in this paper, an automated fault diagnosis technique based on a novel information theoretic (IT)-derived-feature set and an artificial intelligence technique called as error-correcting output codes-support vector machine (ECOC-SVM) is proposed. The gear vibration signals are first processed by continuous wavelet transform to obtain the corresponding time-frequency distributions (TFDs). The TFDs of the faulty signals are then discriminated from those of the healthy ones by introducing IT measures, namely, Kulback-Leibller divergence (KLD), Jensen-Shannon divergence (JSD), Jensen-Rényi divergence (JRD), and Jensen-Tsallis divergence (JTD). These uni-dimensional-IT measures are modified to accommodate the bidimensional TFDs, and the resultant features are referred to as bidimensional time-frequency information theoretic divergence (BTF-ITD) features. The BTF-ITD features are then used to train the ECOC-SVM model. Finally, the trained ECOC-SVM model is used for testing the gear faults. The ECOC approach rectifies the biases and errors in SVM model predictions. The experimental results confirm that the proposed approach provides higher classification accuracy than time-domain features; voting-based-multiclass SVM; and deep learning techniques, such as those based on the stacked sparse autoencoder (SSAE), deep neural network (DNN), and convolution neural network (CNN).