Statistical process monitoring based on just-in-time feature analysis

Abstract Different from traditional analytical algorithms which usually extract representative signature only to characterize the variation in normal operating dataset, the proposed just-in-time feature analysis (JITFA) algorithm is derived to implement latent feature analysis in a just-in-time manner for every single online sampled data, so that the deviation between the online sampled data and the normal operating dataset could be timely uncovered. On the basis of the JITFA algorithm, the corresponding implementation for multivariate statistical process monitoring (MSPM) can timely extract a specific latent feature which exposes the inconsistency covered in the online sampled data to the maximal extent. The superiority and effectiveness of the JITFA-based MSPM approach are demonstrated through comparisons with a total of 11 counterparts including both static and dynamic MSPM methods. In addition to the salient monitoring performance, the proposed method does not involve any other pre-determined model parameters, which would further make itself to be more easily applicable in contrast to the classical and state-of-the-art approaches.