Fault diagnosis of rotating machinery using novel self-attention mechanism TCN with soft thresholding method

Abstract

Abstract Rotating machinery (e.g. rolling bearings and gearboxes) is usually operated in high-risk and vulnerable environments such as time-varying loads and poor lubrication. Timely assessment of the operational status of rotating machinery is crucial to prevent damage caused by potential failure and shutdown, which significantly enhances the reliability of mechanical systems, prolongs the service life of critical components in rotating machinery, and minimizes unnecessary maintenance costs. In this regard, in this paper, a novel approach named self-attention mechanism combining time convolutional network with soft thresholding algorithm (SAM-TCN-ST) is proposed for fault intelligent recognition of rotating machinery. Specifically, the vibration signals are transformed into time-frequency graphs with distinct features utilizing the continuous wavelet transform, and then the proposed SAM-TCN-ST algorithm is employed for capturing essential data characteristics and classification performance. Eventually, datasets from rolling bearings and gearboxes are used to verify the accuracy and effectiveness of the proposed method compared with state-of-the-art benchmark networks such as pure TCN, convolutional neural networks and long short-term memory models. Experimental results demonstrate that the recognition accuracy rate of the proposed SAM-TCN-ST is higher than that obtained from the benchmark methods. This research presents an intelligent and viable solution for achieving real-time monitoring of the status and detecting faults in rotating machinery, thereby expectedly enhancing the reliability of mechanical systems. Consequently, the proposed SAM-TCN-ST algorithm holds significant potential for applications in prognostic and health management practices related to rotating machinery.