A Novel Two-Stage Fault-Detection Method Based on Constrained RVM and Integrating LDA With Minimax Probability Machine

This article presents a novel two-stage fault-detection (FD) method composed of a preclassifier and a reclassifier for complex industrial processes, where the preclassifier is developed by combining linear discriminant analysis and minimax probability machine to reduce dimensionality and classify fully separable data with low computation time. For overlapping data that cannot be separated by the preclassifier, a reclassifier is designed by constructing a constrained relevance vector machine (RVM), according to Neyman–Pearson principle, to decrease the missed alarm rate. The reclassifier has a lower computational load than traditional RVM due to the amount and dimensionality of reclassified data reduced by the first stage, thereby a balance between detection accuracy and computational burden of the whole FD method can be achieved. Finally, an industrial benchmark of Tennessee–Eastman process is utilized to verify the effectiveness of the proposed FD method.

I. Introduction

Fault detection (FD) is an active research area in modern process industries to enhance system security. Numerous FD methods have been proposed for various requirements, which can be mainly divided into data-based and model-based FD methods [1]–[3]. For complex industrial processes, constructing a precise mathematical system model is generally impractical. Therefore, data-driven FD techniques are widely studied in process industries because of their effectiveness and ease of deployment.