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Joint Data-Driven Fault Diagnosis Integrating Causality Graph With Statistical Process Monitoring for Complex Industrial Processes | IEEE Journals & Magazine | IEEE Xplore

Joint Data-Driven Fault Diagnosis Integrating Causality Graph With Statistical Process Monitoring for Complex Industrial Processes


Fault diagnosis scheme based on causality graph and statistical process monitoring.

Abstract:

In this paper, an integrated fault diagnosis method is proposed to deal with fault location and propagation path identification. A causality graph is first constructed fo...Show More
Topic: Data-Driven Monitoring, Fault Diagnosis and Control of Cyber-Physical Systems

Abstract:

In this paper, an integrated fault diagnosis method is proposed to deal with fault location and propagation path identification. A causality graph is first constructed for the system according to the a priori knowledge. Afterward, a correlation index (CI) based on the partial correlation coefficient is proposed to analyze the correlation of variables in causality graph quantitatively. To achieve accurate fault detection results, the proposed CI is monitored by probability principal component analysis. Moreover, the concept of weighted average value is introduced to identify fault propagation path based on reconstruction-based contribution and causality graph after detecting a fault. Finally, the new proposed scheme would be practiced with real industrial HSMP data, where the individual steps as well as the complete framework were extensively tested.
Topic: Data-Driven Monitoring, Fault Diagnosis and Control of Cyber-Physical Systems
Fault diagnosis scheme based on causality graph and statistical process monitoring.
Published in: IEEE Access ( Volume: 5)
Page(s): 25217 - 25225
Date of Publication: 25 October 2017
Electronic ISSN: 2169-3536

Funding Agency:


References

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