摘要
Fault isolation is an important step in multivariate statistical process monitoring, aiming to discovering the process variables critical to the detected fault. However, there are certain shortcomings limiting the implements of the existing methods. Contribution plots often suffer from the smearing effect, while reconstruction analysis requires known fault directions or a large amount of historical fault data that is often unavailable. The performance of the recent developed variable selection method depends on the availability of a historical dataset collected under normal operating conditions and without outlier. Such a dataset is difficult to acquire in real industries. In this research, a robust matrix recovery method called stable principal component pursuit (SPCP) is utilized to solve such problems, which decomposes the data matrix containing both historical operating data and fault measurements into three parts: low-rank process characteristics, sparse errors, and dense noise. In doing so, the variables contributing most to the fault can be identified according to the estimated sparse matrix. The isolation results are robust to the existence of outliers contained in the historical dataset.