Fault Isolation Based on k-Nearest Neighbor Rule for Industrial Processes

Recently, the well-known k-nearest neighbor (kNN) rule has been successfully applied to the fault detection of industrial processes with nonlinear, multimode, and non-Gaussian distributed data. Once a fault is detected, how to investigate the root causes of the fault by isolating the true faulty variables without any historical fault information is a challenging problem. Inspired by the idea of the contribution analysis (CA) methods developed in the frame of the principal component analysis (PCA), in this paper, a novel isolation index will be provided by decomposing the kNN distance used as the detection index in kNN-based fault detection method. The commonly used CA-based isolation methods suffer from smearing effect due to the correlation among the defined isolation indices, thus prone to misdiagnosis. The proposed isolation index is defined in the original measurement space without correlation. Moreover, theoretical analysis of the isolability for the proposed fault isolation method shows that it can isolate multiple sensor faults under a less strict condition than that used in the analysis of those CA-based fault isolation methods. The numerical examples and Tennessee Eastman (TE) benchmark process are used to illustrate the effectiveness of the proposed method.