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Article

A Correlation Driven Approach with Edge Services for Predictive Industrial Maintenance

by 1,2,3,* and 2,3
1
School of Computer Science and Technology, Tianjin University, Tianjin 300350, China
2
Beijing Key Laboratory on Integration and Analysis of Large-Scale Stream Data, North China University of Technology, Beijing 100144, China
3
Institute of Data Engineering, North China University of Technology, Beijing 100144, China
*
Author to whom correspondence should be addressed.
Sensors 2018, 18(6), 1844; https://doi.org/10.3390/s18061844
Received: 29 April 2018 / Revised: 26 May 2018 / Accepted: 31 May 2018 / Published: 5 June 2018
(This article belongs to the Special Issue Internet of Things and Ubiquitous Sensing)
Predictive industrial maintenance promotes proactive scheduling of maintenance to minimize unexpected device anomalies/faults. Almost all current predictive industrial maintenance techniques construct a model based on prior knowledge or data at build-time. However, anomalies/faults will propagate among sensors and devices along correlations hidden among sensors. These correlations can facilitate maintenance. This paper makes an attempt on predicting the anomaly/fault propagation to perform predictive industrial maintenance by considering the correlations among faults. The main challenge is that an anomaly/fault may propagate in multiple ways owing to various correlations. This is called as the uncertainty of anomaly/fault propagation. This present paper proposes a correlation-based event routing approach for predictive industrial maintenance by improving our previous works. Our previous works mapped physical sensors into a soft-ware-defined abstraction, called proactive data service. In the service model, anomalies/faults are encapsulated into events. We also proposed a service hyperlink model to encapsulate the correlations among anomalies/faults. This paper maps the anomalies/faults propagation into event routing and proposes a heuristic algorithm based on service hyperlinks to route events among services. The experiment results show that, our approach can reach 100% precision and 88.89% recall at most. View Full-Text
Keywords: sensor data; event correlations; proactive data service; service hyperlink; edge computing sensor data; event correlations; proactive data service; service hyperlink; edge computing
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MDPI and ACS Style

Zhu, M.; Liu, C. A Correlation Driven Approach with Edge Services for Predictive Industrial Maintenance. Sensors 2018, 18, 1844. https://doi.org/10.3390/s18061844

AMA Style

Zhu M, Liu C. A Correlation Driven Approach with Edge Services for Predictive Industrial Maintenance. Sensors. 2018; 18(6):1844. https://doi.org/10.3390/s18061844

Chicago/Turabian Style

Zhu, Meiling, and Chen Liu. 2018. "A Correlation Driven Approach with Edge Services for Predictive Industrial Maintenance" Sensors 18, no. 6: 1844. https://doi.org/10.3390/s18061844

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