Diagnosis of multiple and unknown faults using the causal map and multivariate statistics

• 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.

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.