A Correlation Driven Approach with Edge Services for Predictive Industrial Maintenance
AbstractPredictive 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
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Zhu, M.; Liu, C. A Correlation Driven Approach with Edge Services for Predictive Industrial Maintenance. Sensors 2018, 18, 1844.
Zhu M, Liu C. A Correlation Driven Approach with Edge Services for Predictive Industrial Maintenance. Sensors. 2018; 18(6):1844.Chicago/Turabian Style
Zhu, Meiling; Liu, Chen. 2018. "A Correlation Driven Approach with Edge Services for Predictive Industrial Maintenance." Sensors 18, no. 6: 1844.
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