Contrastive Learning Framework With Cross-Sensor Adaptive Signal Representation for Fault Diagnosis

Although multisource sensor (MS) signal-based mechanical fault diagnosis (MFD) can significantly improve the diagnostic performance, the existing methods often lack sufficient adaptability and generalization when retraining on single-sensor signals or inferring from partial sensor signals. Thus, a g...

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Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems Vol. 36; no. 10; pp. 17801 - 17813
Main Authors: He, Wenbin, Mao, Jianxu, Wang, Yaonan, Li, Zhe, Zhang, Hui
Format: Journal Article
Language:English
Published: United States IEEE 01.10.2025
Subjects:
Adaptation models
Adaptive signal representation
Contrastive learning
Fault diagnosis
Feature extraction
mechanical fault diagnosis (MFD)
Monitoring
multisource sensor (MS)
Robot sensing systems
Signal representation
supervised contrastive learning
Training
Vectors
Visual perception
ISSN:2162-237X, 2162-2388, 2162-2388
Online Access:Get full text
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Summary:Although multisource sensor (MS) signal-based mechanical fault diagnosis (MFD) can significantly improve the diagnostic performance, the existing methods often lack sufficient adaptability and generalization when retraining on single-sensor signals or inferring from partial sensor signals. Thus, a general two-stage signal representation contrastive learning fault diagnosis framework (T-SCF) is proposed to adapt the trained model to varying numbers of sensor signals. This framework enhances model robustness and data fusion by comparing sensor signal views, offering a new approach for information fusion, fault detection, and classification in MFD. In the first stage, an adaptive contrastive algorithm is proposed to generate contrastive samples (C-Ss) and contrastive labels (C- L s) for MS signals. Then, a supervised contrastive loss (SCL) is designed to minimize the similarity between different fault MS signals while maximizing the similarity between identical ones. By designing a parallel encoder architecture, SCL enables it to merge contrasting the features of different sensor signals during training. This strategy preserves the time-domain dimension properties of different sensors during the training of the second-stage classifier, thereby improving the adaptability of the model to different sensor signals without affecting the global information. The effectiveness of the method was verified from multiple different evaluation dimensions using two public datasets and one self-built dataset.
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ISSN:2162-237X
2162-2388
2162-2388
DOI:10.1109/TNNLS.2025.3582858