A MPRM-based approach for fault diagnosis against outliers

In practice industrial applications, the training dataset collected from production process usually contain some outliers. This is a difficult case for partial least squares (PLS) regression which is sensitive to outliers. In order to solve this problem, the paper presents a multivariate statistical process monitoring and diagnosis method based on modified partial robust M-regression (MPRM). MPRM can eliminate the effects of the outliers in the raw data and establish accurate model by down-weighting the outliers strategy. In industry application, the key performance indicator related prediction and fault diagnosis are important to ensure the product quality and enhance economic benefits. Although, the standard PLS is one kind of the most widely used method, it performs an oblique projection to the input space, and thus cannot well distinguish quality-related faults from quality-unrelated ones. To overcome this drawback, the paper proposes to use the singular value decomposition (SVD) on coefficient matrix to decompose input data into quality-related part and quality-unrelated part. Then, robust monitoring statistics and control limits are derived for process monitoring purposes. A numerical example illustrates that the proposed method shows superior process monitoring performance in comparison with standard PLS when the modeling dataset contains outliers.