A multi-fault diagnosis method of gear-box running on edge equipment

Abstract

Edge computing equipment is a new tool that has been widely used to monitor the operation state of industrial equipment and to diagnose and analyze faults. Therefore, the fault diagnosis algorithm used in the edge computing device plays an especially significant role in fault diagnosis. The application of deep learning method in mechanical fault diagnosis has been gradually popularized, because it has many advantages, such as strong classification ability and accurate feature extraction ability. However, many of the completed papers and models are based on single label system and are used to diagnose single target fault. The validation set is not rigorous enough, and it is difficult to accurately simulate the faults that may occur in the actual production process. Nowadays, in the era of big data, the single label system ignores the joint relationship of different fault types, and it is difficult to make a correct judgment for the location, type and degree of mechanical failure. Hence, in the process of experiment, we used the bearing data of Case Western Reserve University(CWRU) to ensure the wide range and large quantity of data sets. A fault diagnosis method of gear and bearing in the gear-box based on multi-task deep learning model is put forward. In this method, gear and bearing faults can be diagnosed simultaneously. Through a separate task layer, this method can adaptively extract the characteristics of distinct targets from the same signal, and add a Batch Normalization layer(BN) to accelerate the convergence speed of the network. Through experiments, we conclude that it is an effective method which can judge the fault situation of gear and bearing accurately in a variety of working conditions.