Cross-Sensor Generative Self-Supervised Learning Network for Fault Detection Under Few Samples

Huijuan Zhu

doi:10.1007/s11424-025-4001-2

Cross-Sensor Generative Self-Supervised Learning Network for Fault Detection Under Few Samples

2025-06-09

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DOI：10.1007/s11424-025-4001-2

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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.

Keywords:

Fault detection

generative self-supervised learning

multi-dimension cross-sensor

multi-sensor

pretraining.

Cross-Sensor Generative Self-Supervised Learning Network for Fault Detection Under Few Samples

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