Abstract
Fault detection is a key element to ensure continuous operation of equipment. It is important to detect new emerging fault types of equipment in time and classify it. Existing fault detection methods are only suitable for detecting and classifying known faults, and cannot detect and classify new emerging unknown faults efficiently. By using the anomaly detection principle of the iForest, this paper proposes an effective classification method for both known and unknown faults. By extracting features from raw data, constructing a completely random forest, we achieve the classification for known faults. Then by setting reasonable partition boundaries according to the distribution characteristics of known anomaly data, we achieve the classification for unknown faults. Real data based simulations show the feasibility and effectiveness of our proposed classification method.
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Acknowledgement
This work is supported by the National Natural Science Foundation of China (Grant No. 61672282), the Basic Research Program of Jiangsu Province (Grant No. BK20161491) and the State Grid Corporation Science and Technology Project (Contract No.: SGLNXT00YJJS1800110).
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Su, S., Chang, X., Qiu, Y., Li, J., Li, T. (2019). Equipment Fault Detection Based on SENCForest. In: Sun, X., Pan, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2019. Lecture Notes in Computer Science(), vol 11634. Springer, Cham. https://doi.org/10.1007/978-3-030-24271-8_3
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DOI: https://doi.org/10.1007/978-3-030-24271-8_3
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