A Plug-and-Play New Fault Detection Method Based on Deep Compressed Sensing and Contrastive Learning

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Abstract
OOD detection mechanism is important when en-countering new (out-of-distribution, OOD) faults. Current OOD detection methods are often coupled with diagnostic models, lim-iting their actual deployment. Moreover, they usually assume that OOD faults exhibit significant distributional differences from the in-distribution (ID) data, and the ability to magnify differences is insufficient, affecting the detection ability. There-fore, we propose a reconstruction-based method using deep com-pressed sensing (DCS) and contrastive learning (CL), which is plug-and-play and can magnify the differences without OOD fault samples during training. The frequency-domain signal first (1) passes through the measurement module initialized by CL to align ID semantics, then (2) is reconstructed differently by the reconstruction module centered on global-local feature recon-struction blocks with local feature frequency-domain attention (LFA-GLFR blocks). (3) With the optimization objective of joint reconstruction accuracy, sparsity, and ID error consistency, the parameter training is guided to move in the direction of increas-ing differences to achieve better discrimination. We conduct ex-periments on the Shandong University (SDU) bearing dataset and the University of Connecticut (UoC) gearbox dataset. On the former, its average AUROC, AUPRC, F1-score, and F0.5-score are 0.1303, 0.1645, 0.0850, and 0.1156 higher than those of the compared state-of-the-art (SOTA) methods, demonstrating its advancement.
Funding Information
  • Jiangsu Provincial Key Research and Development Program (2022CXGC020204)
  • Jiangsu Provincial Key Research and Development Program (2022CXGC010101)
  • National Key Research and Development Program of China (2022YFB4702500)

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