Abstract
Predictive maintenance aims at enabling proactive scheduling of maintenance, and thus prevent unexpected equipment failures. Most approaches focus on predicting failures occurring within individual sensors. However, a failure is not always isolated. It probably formed by propagation of trivial anomalies, which are widely regarded as events, among sensors and devices. In this paper, we propose an event correlation discovery algorithm to capture correlations among anomalies/failures. Such correlations can show us lots of clues to the propagation paths. We also extend our previous service hyperlink model to encapsulate such correlations and propose a service-based predictive maintenance approach. Moreover, we have made extensive experiments to verify the effectiveness of our approach.
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Funding: This work was supported by National Natural Science Foundation of China (No. 61672042).
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Zhu, M., Liu, C., Han, Y. (2018). An Event Correlation Based Approach to Predictive Maintenance. In: Cai, Y., Ishikawa, Y., Xu, J. (eds) Web and Big Data. APWeb-WAIM 2018. Lecture Notes in Computer Science(), vol 10988. Springer, Cham. https://doi.org/10.1007/978-3-319-96893-3_18
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DOI: https://doi.org/10.1007/978-3-319-96893-3_18
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