Intelligent Fault Diagnosis of Rotating Machinery Based on Deep Neural Network

Abstract In order to improve the fault diagnosis for rotating machinery in nuclear power plants, this paper proposes a fault diagnosis model based on short-time Fourier transform (STFT) and Residual Network (ResNet), using information from multiple sensors. Firstly, acceleration sensors are installed on the different positions of the motor to measure vibration signals, and the normal condition and eight typical faults of the motor are simulated by the mechanical fault simulation test platform. Then, sensor signals from different positions of the motor are obtained as inputs to the fault diagnosis model to reduce the impact of the fault feature attenuation during mechanical propagation. In data preprocessing, STFT is used to convert the multi-source signals from time domain to time-frequency domain to extract the fault features of the signals. Finally, ResNet is used to further extract advanced features from time-frequency feature signals and identify fault patterns. Results obtained show that the proposed model based on STFT, ResNet and multi-sensor information strategy can accurately classify the fault modes, and comparisons with the traditional convolutional network model and single sensor strategy model highlight the advantages of the proposed method in rate of convergence, accuracy and noise robustness.