Deep Residual Network for Identifying Bearing Fault Location and Fault Severity Concurrently

Fault diagnosis is composed of two tasks, i.e., fault location detection and fault severity identification, which are both significant to equipment maintenance. The former can indicate where the defective component lies in, and the latter provides evidence on the residual life of the component. However, traditional fault diagnosis methods, like the time-based methods, frequency-based methods and time-frequency-based methods, can only achieve one goal every time. They are not able to produce highly representative features for dealing with above-mentioned two tasks simultaneously. In addition, there is a huge increase in the amount of monitoring data of equipment. There is urgent need for handling this massive data, obtaining highly discriminative features, and further producing accurate diagnosis results in the field of fault diagnosis. Aimed at these problems, a deep residual network based on multi-task learning is proposed, taking detection of fault location and judgment of fault severity into account simultaneously. This network is fed with two kinds of diagnostic information, which is helpful to mine the potential links between two tasks of fault diagnosis and generate very representative features. Moreover, based on maximizing activation value, a visualization method of role of deep neural network is proposed. It can break in the traditional way of using deep neural network as black box. A real bearing experiment validates that the proposed method is reliable and effective in bearing fault diagnosis.