A semisupervised fault frequency analysis method for rotating machinery based on restricted self-attention network

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With the development of informatization and digitalization, condition monitoring has been applied to industrial equipment such as rotating machinery. Collecting and storing large amounts of equipment operating data enable the detection of mechanical equipment faults using historical operational data. This article proposes a semisupervised data-driven approach to analyze the fault frequencies of rotating machinery. Frequency band information and the degree of association with faults are obtained through the variance of attention values. To address the inherent issue of decoupling information between data segments in deep learning, restrictive layers are proposed. These layers prevent the flow of information between data segments from rendering interpretable information ineffective. Bearing and gearbox datasets are used to validate the proposed method. The fault frequencies extracted by this method correspond to actual faults. The preferred deep learning framework achieves an accuracy exceeding 99% on both datasets. The method is compared with various signal processing methods and identifies fault frequencies that are difficult to identify using traditional methods. Furthermore, the unreliability of traditional deep learning in fault diagnosis is also exposed. In this study, semisupervised deep learning fault frequency extraction is achieved for the first time.