Incipient fault detection and variable isolation based on subspace decomposition and distribution dissimilarity analysis

Abstract

The conventional multivariate statistical process control (MSPC) methods may not be sensitive to the detection of incipient changes since they in general quantify the distance between the new sample and the modeling samples without checking the changes of data distribution. In the present works, a method with dissimilarity analysis and quality-relevant subspace decomposition based on process monitoring method is developed to detect incipient abnormal behaviors that cannot be readily picked up by the conventional MSPC. First, the data are divided into quality-relevant subspace and the other subspace. Then dissimilarity analysis is performed to quantitatively evaluate the distribution difference between the normal condition and fault status for both subspaces. It can evaluate the incipient abnormal behaviors from the quality-relevant perspective to reveal the influences of incipient abnormality on quality. The paper demonstrates that the new method is more sensitive to the detection and isolation of incipient abnormal behaviors that are responsible for the distortion of the underlying covariance structure. Besides, it can tell whether the incipient fault can influence the quality index or not. Its feasibility and performance are illustrated with industrial process data.