A New Compound Fault Diagnosis Method for Gearbox Based on Convolutional Neural Network

Abstract

This paper focus on the fault diagnosis problem for the compound faults of rotating machine, in which the rolling bearing and the sun gear faults simultaneously occurred are considered as the compound fault. Considering the traditional compound fault diagnosis methods usually utilize the manual fault features extraction, which are mainly dependent on engineering experience, we propose a compound fault diagnosis method named multi-sensor based convolutional neural network (MCNN). For vibration signals of compound faults, the different transmission paths and the positions of the sensors means one part of the embedded single faults may have higher energy. The vibration signals collected from three sensors at different positions can help guarantee the completeness of the characteristics of the compound fault. Then, the multi-sensor signals are combined together and fused by the convolutional operation of the convolutional neural network (CNN) model. The CNN model, which can automatically extract features from the vibration signals and achieve classification, is used for fault extraction and fault recognition. The experiments are presented on the physical platform of power transmission, and the proposed fault diagnosis method can be verified with the satisfied performance.