An incipient fault diagnosis methodology using local Mahalanobis distance: Fault isolation and fault severity estimation

Abstract

Recently, a detection methodology based on anchor estimation and the Mahalanobis distance, named the local Mahalanobis distance (LMD) technique, has been proposed for incipient fault detection. As an extension, this paper proposes the LMD based faulty variable isolation and fault severity estimation methods to offer an integrated incipient fault diagnosis solution. The LMD technique has been proved to be highly sensitive to incipient faults, robust to outliers, and free of distribution assumptions. Within the LMD framework, the faulty variable is recognized by analyzing the relative position of faulty samples and their corresponding anchors. Then an analytical expression of fault severity derived from the LMD index is established for the fault severity estimation task. Performance evaluations of the proposed methods based on a benchmark case of the Continuous-flow Stirred Tank Reactor (CSTR) process are provided in this work. The result shows that even for tiny deviation, such as 5dB fault to noise ratio, the total isolation performance still achieves 100% accuracy, and the relative error of two tricky faults in the case of severity estimation is less than 10%. The comparison study of different methods indicates that our solution outperforms divergence based techniques and reconstruction based contribution (RBC) methods for the two tasks.