Research on the service condition monitoring method of rolling bearings based on adaptive deconvolution and transformer

Abstract

Abstract Aiming at the complexity of the rolling bearing operation state in actual working conditions and the limitation of the feature extraction capability of single-scale model, this paper proposes a fault diagnosis method based on two-channel adaptive deconvolution and transformer module (TADAT). First, a frequency domain dataset is built and multiscale feature information is extracted using two-channel adaptive deconvolution at the beginning stage of the model; the global features of the signal are further extracted by combining them in series with the transformer (multi-head attention). Next, the loss function is optimized by introducing a regularized loss function with label smoothing and L2 regularization penalty term. Finally, the model is validated against an open-source dataset and operating condition data collected from an unbalanced bearing load test bed with an accuracy of over 98.5%. The model also maintains high accuracy under noisy samples. The test shows that the rolling bearing service condition monitoring method based on TADAT feature extraction model proposed by this method is characterized by simple model structure, high diagnostic accuracy and strong generalization performance, which provides a useful reference for the research of rolling bearing service condition monitoring.