An unsupervised anomaly detection approach using spatiotemporal feature in hot strip rolling

Cong Liu; Kai Zhang

doi:10.1117/12.2686573

10 August 2023 An unsupervised anomaly detection approach using spatiotemporal feature in hot strip rolling

Cong Liu, Kai Zhang

Proceedings Volume 12759, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2023); 127593E (2023) https://doi.org/10.1117/12.2686573
Event: 2023 3rd International Conference on Automation Control, Algorithm and Intelligent Bionics (ACAIB 2023), 2023, Xiamen, China

Abstract

In the hot strip rolling process, anomalies can potentially have a considerable impact on the overall production process owing to the interdependent nature of the production module and variables. To address this concern, this paper proposes a data-driven framework that leverages a spatiotemporal feature extraction method based on Symbolic Dynamic Filtering to identify the causal relationships among the variables. These extracted features are then used to learn more representational features via a Restricted Boltzmann Machine, and the monitoring statistics are built on its concept of energy to form an anomaly detection method. The experimental findings demonstrate the efficacy and feasibility of the proposed framework in detecting faults, exhibiting notable accuracy and promptness in real-time scenarios.

Citation Download Citation

Cong Liu and Kai Zhang "An unsupervised anomaly detection approach using spatiotemporal feature in hot strip rolling", Proc. SPIE 12759, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2023), 127593E (10 August 2023); https://doi.org/10.1117/12.2686573

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KEYWORDS

Feature extraction

Education and training

Tunable filters

Data modeling

Complex systems

Data storage

Machine learning

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