Abstract
Predictive maintenance aims at enabling proactive scheduling of maintenance, and thus prevents unexpected equipment failures. Most approaches focus on predicting failures occurring within individual sensors. However, a failure is not always isolated. The complex dependencies between different sensors result in complex temporal dependencies across multi anomaly events. Therefore, mining such temporal dependencies are valuable as it can help forecast future anomalies in advance and identifying the possible root causes for an observable anomaly. In this paper, we transform the temporal dependency mining problem into a frequent co-occurrence pattern mining problem and propose a temporal dependency mining algorithm to capture temporal dependency among multi anomaly events. Finally, we have made a lot of experiments to show the effectiveness of our approach based on a real dataset from a coal power plant.
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Acknowledgement
This work is supported by “The National Key Research and Development Plan (No: 2017YFC0804406), Public Safety Risk Prevention and Control and Emergency Technical equipment”, and the “Key Project of the National Natural Science Foundation of China No. 61832004 (Research on Big Service Theory and Methods in Big Data Environment)”.
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Cao, W., Liu, C., Han, Y. (2019). Temporal Dependency Mining from Multi-sensor Event Sequences for Predictive Maintenance. In: Ni, W., Wang, X., Song, W., Li, Y. (eds) Web Information Systems and Applications. WISA 2019. Lecture Notes in Computer Science(), vol 11817. Springer, Cham. https://doi.org/10.1007/978-3-030-30952-7_27
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DOI: https://doi.org/10.1007/978-3-030-30952-7_27
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