Partial Sliced Inverse Regression for Quality-Relevant Multivariate Statistical Process Monitoring

TL;DR

This paper proposes a novel partial sliced inverse regression (PSIR) method for multivariate process monitoring, effectively handling nonlinear, large, or small sample datasets, and demonstrating superior fault detection performance over existing methods.

Contribution

It extends sliced inverse regression (SIR) with a partial approach inspired by PLS, integrating information from predictors and responses for improved process monitoring.

Findings

PSIR outperforms PLS and SIR in simulations.

PSIR effectively handles nonlinear and large datasets.

Provides new control limits for fault detection.

Abstract

This paper introduces a popular dimension reduction method, sliced inverse regression (SIR), into multivariate statistical process monitoring. Provides an extension of SIR for the single-index model by adopting the idea from partial least squares (PLS). Our partial sliced inverse regression (PSIR) method has the merit of incorporating information from both predictors (x) and responses (y), and it has capability of handling large, nonlinear, or "n<p" dataset. Two statistics with their corresponding distributions and control limits are given based on the X-space decomposition of PSIR for the purpose of fault detection in process monitoring. Simulations showed PSIR outperformed over PLS and SIR for both linear and nonlinear model.