A new reconstruction-based method using local Mahalanobis distance for incipient fault isolation and amplitude estimation

Faulty variable isolation and amplitude estimation are of great importance to support the decision-making for system maintenance but lack sufficient studies, especially concerning the challenge of incipient faults with tiny amplitude. The reconstruction-based contribution (RBC) idea is commonly used for faulty variable isolation and fault amplitude estimation but usually suffers from low accuracy performance when facing the incipient faults challenge due to the use of insensitive detection indexes. Therefore, this paper proposes an improved reconstruction-based approach using the highly sensitive index named local Mahalanobis distance (LMD) for incipient fault isolation and amplitude estimation. The novel RBC approach retains the advantages of LMD, e.g., high sensitivity to incipient faults, robustness to outliers, and ability to handle non-Gaussian data, and is also available for multiple faulty variables isolation. The performance evaluation of the proposed methods using the benchmark case of the Continuous-flow Stirred Tank Reactor (CSTR) process shows that this approach has high isolation and estimation accuracy for both single and multiple faults. Thanks to a comparative study, it can be highlighted that for both faulty variables isolation and fault amplitude estimation tasks, our proposal outperforms the state-of-the-art RBC-based methods using different detection indexes: the combined index of PCA, conventional Mahalanobis distance, and augmented Mahalanobis distance.