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. 2018 Jun 5;18(6):1844.
doi: 10.3390/s18061844.

A Correlation Driven Approach with Edge Services for Predictive Industrial Maintenance

Meiling Zhu  1   2   3 , Chen Liu  4   5
Affiliations

A Correlation Driven Approach with Edge Services for Predictive Industrial Maintenance

Meiling Zhu et al. Sensors (Basel). .

Abstract

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.

Keywords: edge computing; event correlations; proactive data service; sensor data; service hyperlink.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Partial anomaly propagation under correlations among sensors and devices in a coal power plant.
Figure 2
Figure 2
The framework of our approach.
Figure 3
Figure 3
An example of Proactive Data Service Graph (PDSG).
Figure 4
Figure 4
Workflow of our predictive industrial maintenance approach.
Figure 5
Figure 5
Illustration of π[k] Satisfying Some Metric Temporal Logic (MTL) Formulae.
Figure 6
Figure 6
Conditions for a trace π’ of a PDSS to satisfying an OR/AND node on a PDSG.
Figure 7
Figure 7
Variation of correlation number and hyperlink number on different datasets with p ≥ 0.8.
Figure 8
Figure 8
The precision and recall of our approach on different datasets.
Figure 9
Figure 9
Average latency under edge computing and cloud computing on different synthetic datasets.
See this image and copyright information in PMC

References

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