Polymer Gear Fault Classification Using EMD-DWT Analysis Based on Combination of Entropy and Hjorth Features

Polymer gears have proven to be an adequate replacement for traditional metal gears in various applications. They are lighter, have less inertia, and are much quieter than their metal counterparts. Polymer gears, however, are rarely employed because there is a lack of failure data. Hence, there is tremendous scope for fault detection of polymer gears. In this paper, a novel technique of polymer gear fault detection is proposed following the double decomposition of vibration signals. The experimentally acquired vibration signals are processed through two steps of decomposition, i.e., empirical mode decomposition and discrete wavelet transform based Time-Frequency decomposition. Subsequently, entropy features (EF), Hjorth parameter (HP), and a combination of EF and HP are extracted. A combination of these feature sets is used to train the classifier: support vector machine (SVM), ensemble learning, and decision tree. Among all classification methods, the ensemble learning classifier reached the maximum classification accuracy of 99.2 % using a combination of EF and HP features. Furthermore, EMD and DWT are compared with the proposed double decomposition method (EMD-DWT) for accuracy validation. The experiments demonstrated that the proposed EMD-DWT method is efficient and yields promising results for classifying polymer gear faults.