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

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.

Automated summary

A method called multisensor cycle-supervised convolutional neural network (CsCNN) is proposed for unsupervised anomaly detection in magnetic flux leakage (MFL) multisensor signals. The CsCNN includes multiple CNNs with the same structure and a cycle-supervised part, allowing for the establishment of latent relationships between multisensor signals and the application of a dynamic threshold for anomaly detection. Experiments show that the proposed method is effective compared to state-of-the-art methods.