Diagnosis of multiple and unknown faults using the causal map and multivariate statistics. (April 2015)

• Record Type: Journal Article
• Authors: Chiang, Leo H.; Jiang, Benben; Zhu, Xiaoxiang; Huang, Dexian; Braatz, Richard D.
• Is Part Of: Journal of process control. Volume 28(2015:Apr.)
• Page Start: 27
• Page End: 39
• Publication Date: 2015-04
• Subjects: Fault diagnosis -- Feature extraction -- Feature representation -- Multiple faults -- Unknown faults -- Process monitoring -- Chemometrics -- Causal map -- Multivariate statistics; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2015.02.004
• Languages: English
• ISSNs: 0959-1524

Abstract

Highlights: A new algorithm is proposed for diagnosing known, unknown, and multiple faults. The approach combines the data-driven approach with causal analysis. The algorithm is demonstrated for 16 known, 6 unknown, and 105 multiple faults. For independent multiple faults, each individual fault is diagnosed. For dependent multiple faults with one dominating fault, that fault is diagnosed. Abstract: Feature extraction is crucial for fault diagnosis and the use of complementary features allows for improved diagnostic performance. Most of the existing fault diagnosis methods only utilize data-driven and causal connectivity-based features of faults, whereas the important complementary feature of the propagation paths of faults is not incorporated. The propagation path-based feature is important to represent the intrinsic properties of faults and plays a significant role in fault diagnosis, particularly for the diagnosis of multiple and unknown faults. In this article, a three-step framework based on the modified distance (DI) and modified causal dependency (CD) is proposed to integrate the data-driven and causal connectivity-based features with the propagation path-based feature for diagnosing known, unknown, and multiple faults. The effectiveness of the proposed approach is demonstrated on the Tennessee Eastman process.