Efficient fault diagnosis of rolling bearings using neural network architecture search and sharing weights

Abstract

Bearing is one of the most vital components of industrial machinery. The failure of bearing causes severe problems in the machinery. Therefore, continuous monitoring for the bearings is essential rather than regular manual checking, with the requirement for accuracy of prediction and efficiency. This paper proposes a novel intelligent bearing fault condition monitoring and diagnosis method focusing on computation efficiency, which is an important aspect of a continuous monitoring and embedded-based diagnosis device. In the proposed method, acoustic emission signals containing bearing health information are converted into 2-D spectrograms by Constant Q-Transform (CQT) before using a convolutional neural network to infer the bearing state. To reduce the latency while maintaining high accuracy, we propose an efficient search space for neural network architecture search, i.e., a channel distribution search, that automatically obtain the best performing network. Moreover, we present a separation between two processes of condition monitoring and fault diagnosis to save overall computing resources, with a policy of sharing weights in the training process and sharing features in the testing process. The experimental results show that the proposed method reduces about 50% inference time compared to previous methods while achieving an accuracy of 99.82% for eight types of single and compound fault diagnosis for variable rotational speeds.