Fault Detection and Identification Using a Novel Process Decomposition Algorithm for Distributed Process Monitoring

TL;DR

This paper presents a new process decomposition algorithm that enhances fault detection and identification in distributed process monitoring by combining block-level PCA, a contributions map, and fault indices, achieving detection rates comparable to advanced methods.

Contribution

The paper introduces a novel process decomposition algorithm that simplifies fault detection and identification by leveraging process flow structures and PCA at the block level.

Findings

Fault detection rates comparable to advanced methods on benchmark tests.

The decomposition algorithm is straightforward for control engineers to implement.

The contributions map aids in visual fault analysis and propagation understanding.

Abstract

Recent progress in fault detection and identification increasingly relies on sophisticated techniques for fault detection, applied through either centralized or distributed approaches. Instead of increasing the sophistication of the fault detection method, this work introduces a novel algorithm for determining process blocks of interacting measurements and applies principal component analysis (PCA) at the block level to identify fault occurrences. Additionally, we define a novel contributions map that scales the magnitudes of disparate faults to facilitate the visual identification of abnormal values of measured variables and analysis of fault propagation. Bayesian aggregate fault index and block fault indices vs. time pinpoint origins of the fault. The proposed method yields fault detection rates on par with most sophisticated centralized or distributed methods on the Tennessee Eastman Plant benchmark. Since the decomposition algorithm relies on the process flowsheet and control loop structures, practicing control engineers can implement the proposed method in a straightforward manner.