Diagnosis of multiple and unknown faults using the causal map and multivariate statistics. (April 2015) - Chiang, Leo H.; Jiang, Benben; Zhu, Xiaoxiang; Huang, Dexian; Braatz, Richard D.
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.