Fault Diagnosis of Synchronous Belt of Machine Tool Based on Improved Back Propagation Neural Network

Abstract

Aiming at the problem that the machine tool synchronous belt failure during the transmission process will affect the machine tool transmission, a machine tool synchronous belt fault diagnosis method based on genetic algorithm (GA) optimized back propagation (BP) neural network is proposed. First, utilize wavelet decomposition to extract the energy characteristics of the synchronization belt fault; construct a BP neural network, and use genetic algorithms to optimize the BP neural network; finally, the energy characteristic of the vibration signal of the synchronous belt is used as the input of the neural network, and the fault simulation test is carried out. The results show that the genetic algorithm GA-optimized BP neural network has higher accuracy than the traditional BP neural network for fault diagnosis of machine tool synchronous belt.