Measurement + Control (Aug 2024)

A roundtrip probability estimation method for mechanical equipment fault detection under imbalanced samples

  • Zhang Yuyan,
  • Zhang Yongqi,
  • Ming Wuyi,
  • Li Hao,
  • Wen Xiaoyu,
  • Yan Lingdi

DOI
https://doi.org/10.1177/00202940241237118
Journal volume & issue
Vol. 57

Abstract

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Aiming at high misdetection of mechanical faults under imbalanced samples, a roundtrip probability-based method is proposed. By roundtrip mapping between latent variables and real fault data, biased estimation of the probability distribution of real fault data is obtained. Further, virtual fault data are sampled according to such distribution to increase sample amount. For recognition of real and virtual data, loss function based on binary cross-entropy is designed. For reconstruction between fault data and its roundtrip mapped results, objective function based on mean square error is designed. Thus, it preserves boundary data and avoids too many virtual data in central area. Meanwhile, a strategy for eliminating abnormal samples is designed to reduce boundary deviation. For supporting the advantage of roundtrip, in-depth reasons for misdetection are analyzed from empirical risk and structural risk. Experiments on 30 benchmark imbalanced test sets show that fault detection rate increases after amount enhancement. Additionally, it is verified on blade cracking and bearing fault detection. Results show that F 1 score increases from 0.485 to 0.51 and 0.725 to 0.775 for such two cases.