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A Multisensor Cycle-Supervised Convolutional Neural Network for Anomaly Detection on Magnetic Flux Leakage Signals | IEEE Journals & Magazine | IEEE Xplore

A Multisensor Cycle-Supervised Convolutional Neural Network for Anomaly Detection on Magnetic Flux Leakage Signals


Abstract:

To improve the validity of magnetic flux leakage (MFL) multisensor signals, anomaly detection has become a significant part of MFL signal processing. The anomalies in MFL...Show More

Abstract:

To improve the validity of magnetic flux leakage (MFL) multisensor signals, anomaly detection has become a significant part of MFL signal processing. The anomalies in MFL are uncertain and have no prior information or labels. Therefore, the detection and location of the anomalies become a difficult issue. Regarding the abovementioned problem, we propose an unsupervised method called multisensor cycle-supervised convolutional neural network (CsCNN). The CsCNN is built including multiple CNNs with the same structure and a cycle-supervised part. The proposed model realizes unsupervised anomaly detection through multiple cycle-supervised CNNs for the first time. Moreover, the latent relationship between multisensor signals is established by CsCNN to take full use of multisensor information. Besides, a dynamic threshold is applied to detect anomalies. In the end, experiments on simulated signals and measured signals are conducted, and CsCNN is compared to the state-of-the-art methods. The results show that the proposed method is effective.
Published in: IEEE Transactions on Industrial Informatics ( Volume: 18, Issue: 11, November 2022)
Page(s): 7619 - 7627
Date of Publication: 27 January 2022

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