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.
Highlights
Gear, bearing, shaft, box and other significant parts make up the gear-box
Numerous domestic and foreign researchers specializing in mechanical fault combine deep learning and fault diagnosis to solve practical problems under the inspiration of those studies which are mentioned above
Multi-task deep learning model This paper mainly studies the possible faults of two important components of the gear-box, namely, gear fault and bearing fault
Summary
Gear, bearing, shaft, box and other significant parts make up the gear-box. The gear-box is an indispensable part of industrial production and operation. This paper put forward a multi-label system [22] This system is a deep learning network for multi-task fault diagnosis, which is applied to classifying the fault signals of different categories of bearing and gear by establishing a one-dimensional convolution. Experimental setup Baseline model: At present, no one has solved the problem of multi-target fault diagnosis by using multi-task deep learning method. 192000 192000 192000 192000 192000 192000 192000 192000 sample number and the size of the batch size of the training set, each test model carries out a complete training set data weight as much as possible iterative update This is the reason for the great difference in the number of records. The following is the specific method of data set segmentation
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