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

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. (C) 2015 Elsevier Ltd. All rights reserved.