Fault diagnosis method for gear transmission system based on vibration signal analysis

LIU Xiaojun; REN Wenqing

doi:10.11799/ce202505019

Abstract：Gear transmission device is the core component of mechanical equipment,its operating conditions are complex,and the vibration signals of many structural components overlap,making it more difficult to monitor,which requires in-depth analysis by professionals.In view of the challenges faced by the the gear transmission system monitoring of mining machinery and equipment,we firstly reduced the vibration signals based on the joint noise reduction algorithm of modal preference reconstruction and expression(CEEMDAN),and further constructed a fault diagnosis model based on the long and short-term memory(LSTM)neural network.At the same time,the gray wolf algorithm was improved in three aspects,namely,population initialization method,convergence parameter updating strategy,and location updating strategy,and after performance testing and comparison with other algorithms,it was found that the three improvement measures adopted significantly enhanced the performance of the gray wolf algorithm.Finally,the improved gray wolf algorithm was used to optimize the hyperparameters of the fault diagnosis model.After simulation analysis and experimental verification,the fault diagnosis model proposed in this paper,compared with the traditional model,the diagnostic accuracy and stability was greatly improved,and the average accuracy of identification surpassed 96%.