Integrating novelty detection, neural networks and conventional tools for pattern recognition in multivariate processes

Abstract

Statistical process control (SPC) charts are powerful tools that are used to improve quality, increase uniformity and minimize production costs in manufacturing. Neural networks (NNs) have been successfully applied to interpret univariate SPC charts. Despite the growing importance and popularity of multivariate SPC (MSPC), the published literature on the application of NNs to SPC has not adequately discussed the issues related to application of NNs to MSPC for pattern recognition. This paper uses the chi-squared ( x2) statistic as a compact format for representing the input to NNs specialized in positive identification of selected subclasses of multivariate non-random patterns. It is imperative to maintain the reliability of decisions by such specialist NNs in order to convince process operators of their benefits. Therefore, to address the above requirement, this report also shows how novelty detection (ND) can be used as a filter to allow only relevant abnormal patterns to be processed by appropriate specialist NNs. Finally, a new framework within which specialist NNs can complement the decisions of ND and other MSPC techniques is outlined.