DNOCNet: A Novel End-to-End Network for Induction Motor Drive Systems Fault Diagnosis Under Speed Fluctuation Condition
Junchao Guo, Qingbo He, Fengshou Gu
Abstract
Speed fluctuations are a key issue that directly affects the motor fault diagnosis performance. Deep convolutional neural networks (DCNNet) can automatically perform fault feature extraction and classification, but the diagnostic capability of DCNNet may be reduced under speed fluctuation conditions. To address this shortcoming, this article proposes a novel method called deep nonlinear order-cyclic convolutional network (DNOCNet). First, the nonlinear order-cyclic spectrum analysis layer (NOSAL) is constructed to turn the collected signal into angular domain signals to resolve the speed fluctuations. Subsequently, a frequency domain signal-to-noise ratio-based feature fusion scheme is presented to enhance the modulation components for fault feature learning. Finally, the convolutional neural network is employed to extract fault features from NOSAL by layer-by-layer superposition, and the acquired features are fed into softmax classifier for motor fault identification. Experiments from two induction motor cases under speed fluctuation conditions are utilized to verify the effectiveness of DNOCNet.