An Event Correlation Based Approach to Predictive Maintenance
Author:
Publisher
Springer International Publishing
Link
http://link.springer.com/content/pdf/10.1007/978-3-319-96893-3_18
Reference18 articles.
1. Qiu, H., Liu, Y., Subrahmanya, N.A., Li, W.: Granger causality for time-series anomaly detection. In: 12th IEEE International Conference on Data Mining, pp. 1074–1079. Institute of Electrical and Electronics Engineers Inc., Brussels (2012)
2. Sipos, R., Fradkin, D., Moerchen, F., Wang, Z.: Log-based predictive maintenance. In: 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1867–1876. Association for Computing Machinery, New York (2014)
3. Yan, Y., Luh, P.B., Pattipati, K.R.: Fault diagnosis of HVAC air-handling systems considering fault propagation impacts among components. IEEE Trans. Autom. Sci. Eng. 14(2), 705–717 (2017)
4. Ye, R., Li, X.: Collective representation for abnormal event detection. J. Comput. Sci. Technol. 32(3), 470–479 (2017)
5. Pourmirza, S., Dijkman, R., Grefen, P.: Correlation miner: mining business process models and event correlations without case identifiers. Int. J. Coop. Inf. Syst. 26(2), 1–32 (2017)
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