Fault Diagnosis of Rotating Machinery Based on Evolutionary Convolutional Neural Network - Yihao Bai; Weidong Cheng; Weigang Wen; Yang Liu (Shock and Vibration)
This paper proposes a fault diagnosis method for rotating machinery based on evolutionary convolutional neural network (ECNN). With the time-frequency images as the network input, with the help of the global optimization ability of the genetic algorithm, the structure of the convolutional neural network can evolve autonomously, and the adaptive configuration of the structural hyperparameters for the target task is realized. In this paper, the proposed method is verified by the measured signal of the planetary gearbox. The results show that the proposed method is helpful to obtain a convolutional neural network structure with better performance and achieve higher fault diagnosis accuracy.