Structured Sparsity Modeling for Improved Multivariate Statistical Analysis based Fault Isolation

TL;DR

This paper introduces a structured sparsity modeling framework for fault isolation in multivariate statistical analysis, enhancing fault variable identification by incorporating process structure information through regularization terms.

Contribution

It proposes a novel fault isolation method using structured sparsity regularization and ADMM optimization, improving fault variable selection accuracy over traditional methods.

Findings

Enhanced fault variable isolation accuracy demonstrated in simulations.

Increased robustness of fault diagnosis by leveraging process structure.

Efficient optimization via ADMM algorithm.

Abstract

In order to improve the fault diagnosis capability of multivariate statistical methods, this article introduces a fault isolation framework based on structured sparsity modeling. The developed method relies on the reconstruction based contribution analysis and the process structure information can be incorporated into the reconstruction objective function in the form of structured sparsity regularization terms. The structured sparsity terms allow selection of fault variables over structures like blocks or networks of process variables, hence more accurate fault isolation can be achieved. Four structured sparsity terms corresponding to different kinds of process information are considered, namely, partially known sparse support, block sparsity, clustered sparsity and tree-structured sparsity. The optimization problems involving the structured sparsity terms can be solved using the Alternating Direction Method of Multipliers (ADMM) algorithm, which is fast and efficient. Through a simulation example and an application study to a coal-fired power plant, it is verified that the proposed method can better isolate faulty variables by incorporating process structure information.