Cross-sensor Generative Self-supervised Learning Network for Fault Detection under Few Samples

ZHU Huijuan, ZHAO Yunbo, YAN Xiaohui, KANG Yu, LIU Binkun

系统科学与复杂性(英文) ›› 2024

PDF(3416 KB)
PDF(3416 KB)
系统科学与复杂性(英文) ›› 2024

Cross-sensor Generative Self-supervised Learning Network for Fault Detection under Few Samples

    ZHU Huijuan1,2, ZHAO Yunbo2,3, YAN Xiaohui2,3, KANG Yu2,3, LIU Binkun3
作者信息 +

Cross-sensor Generative Self-supervised Learning Network for Fault Detection under Few Samples

    ZHU Huijuan1,2, ZHAO Yunbo2,3, YAN Xiaohui2,3, KANG Yu2,3, LIU Binkun3
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摘要

In this paper, a cross-sensor generative self-supervised learning network is proposed for fault detection of multi-sensor. By modeling the sensor signals in multiple dimensions to achieve correlation information mining between channels to deal with the pretext task, the shared features between multi-sensor data can be captured, and the gap between channel data features will be reduced. Meanwhile, in order to model fault features in the downstream task, the salience module is developed to optimize cross-sensor data features based on a small amount of labeled data to make warning feature information prominent for improving the separator accuracy. Finally, experimental results on the public datasets FEMTO-ST dataset and the private datasets SMT shock absorber dataset(SMT-SA dataset) show that the proposed method performs favorably against other STATE-of-the-art methods.

Abstract

In this paper, a cross-sensor generative self-supervised learning network is proposed for fault detection of multi-sensor. By modeling the sensor signals in multiple dimensions to achieve correlation information mining between channels to deal with the pretext task, the shared features between multi-sensor data can be captured, and the gap between channel data features will be reduced. Meanwhile, in order to model fault features in the downstream task, the salience module is developed to optimize cross-sensor data features based on a small amount of labeled data to make warning feature information prominent for improving the separator accuracy. Finally, experimental results on the public datasets FEMTO-ST dataset and the private datasets SMT shock absorber dataset(SMT-SA dataset) show that the proposed method performs favorably against other STATE-of-the-art methods.

关键词

Multi-dimension cross-sensor / Generative self-supervised learning / Pretraining / Multi-sensor / Fault detection

Key words

Multi-dimension cross-sensor / Generative self-supervised learning / Pretraining / Multi-sensor / Fault detection

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导出引用
ZHU Huijuan , ZHAO Yunbo , YAN Xiaohui , KANG Yu , LIU Binkun. Cross-sensor Generative Self-supervised Learning Network for Fault Detection under Few Samples. 系统科学与复杂性(英文), 2024
ZHU Huijuan , ZHAO Yunbo , YAN Xiaohui , KANG Yu , LIU Binkun. Cross-sensor Generative Self-supervised Learning Network for Fault Detection under Few Samples. Journal of Systems Science and Complexity, 2024

基金

This research was supported by the National Natural Science Foundation of China (No. 62173317), and the Key Research and Development Program of Anhui (No. 202104a05020064).
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