Abstract
Anomaly detection of crane operating status is the basis for ensuring its stable operation. The current detection method based on anomaly detection algorithms cannot clearly distinguish normal data from abnormal data. And the high dimensions of the crane operation data and irrelevant dimensions also affect the detection accuracy of anomaly detection algorithms. In this paper, a crane operating status anomaly detection method with visual analytics task flow is proposed. To achieve closed-loop control of cranes, this paper realizes accurate and efficient anomaly detection of crane operation status based on digital twin as the architecture, visual analytics process as the mainline, and anomaly detection algorithms as the basis. In addition, anomaly reasoning is implemented by the expert system. Finally, the accuracy and effectiveness of the method proposed in this paper were confirmed by the contrast experiment.
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Liu, J., Zheng, H., Jiang, Y., Liu, T., Bao, J. (2022). Visual Analytics Approach for Crane Anomaly Detection Based on Digital Twin. In: Luo, Y. (eds) Cooperative Design, Visualization, and Engineering. CDVE 2022. Lecture Notes in Computer Science, vol 13492. Springer, Cham. https://doi.org/10.1007/978-3-031-16538-2_1
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