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

arXiv: https://arxiv.org/abs/2409.11444 · HTML: https://mlsanity.com/paper/2409.11444/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 47 references that are in the map.
- First posted on arXiv 17 Sep 2024.

## Builds on (most cited first, 1 of 1)

- [Fault Detection and Identification using Bayesian Recurrent Neural Networks](https://mlsanity.com/paper/1911.04386/) (2019)

## Built on by (most cited first, 1 of 1)

- [Comparative Evaluation of Kolmogorov-Arnold Autoencoders and Orthogonal Autoencoders for Fault Detection with Varying Training Set Sizes](https://mlsanity.com/paper/2508.02860/) (2025)

## Abstract (arXiv)

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.