Learning a Factorized Orthogonal Latent Space using Encoder-only Architecture for Fault Detection; An Alarm management perspective

TL;DR

This paper presents an encoder-only architecture that factorizes the latent space into orthogonal deterministic and stochastic components, significantly reducing false alarms in industrial fault detection without detection delay.

Contribution

It introduces a novel encoder-based residual design with orthogonal latent space factorization and constraints, improving fault detection robustness and false alarm reduction.

Findings

Achieves nearly zero false alarms and missed detections in experiments.

Effectively decouples stochastic and deterministic process components.

Demonstrates improved fault detection on Tennessee Eastman process.

Abstract

False and nuisance alarms in industrial fault detection systems are often triggered by uncertainty, causing normal process variable fluctuations to be erroneously identified as faults. This paper introduces a novel encoder-based residual design that effectively decouples the stochastic and deterministic components of process variables without imposing detection delay. The proposed model employs two distinct encoders to factorize the latent space into two orthogonal spaces: one for the deterministic part and the other for the stochastic part. To ensure the identifiability of the desired spaces, constraints are applied during training. The deterministic space is constrained to be smooth to guarantee determinism, while the stochastic space is required to resemble standard Gaussian noise. Additionally, a decorrelation term enforces the independence of the learned representations. The efficacy of this approach is demonstrated through numerical examples and its application to the Tennessee Eastman process, highlighting its potential for robust fault detection. By focusing decision logic solely on deterministic factors, the proposed model significantly enhances prediction quality while achieving nearly zero false alarms and missed detections, paving the way for improved operational safety and integrity in industrial environments.