Dynamic fault detection and diagnosis of industrial alkaline water electrolyzer process with variational Bayesian dictionary learning

TL;DR

This paper introduces a robust Bayesian dictionary learning method for fault detection in alkaline water electrolysis, enhancing reliability and interpretability amidst measurement uncertainties.

Contribution

It proposes a novel dynamic variational Bayesian dictionary learning approach combining sparse Bayesian modeling and low-rank VAR for fault detection in AWE processes.

Findings

Effective fault detection and diagnosis demonstrated on industrial hydrogen production data.

Improved robustness to measurement noise and uncertainties.

Enhanced interpretability of fault detection results.

Abstract

Alkaline Water Electrolysis (AWE) is one of the simplest green hydrogen production method using renewable energy. AWE system typically yields process variables that are serially correlated and contaminated by measurement uncertainty. A novel robust dynamic variational Bayesian dictionary learning (RDVDL) monitoring approach is proposed to improve the reliability and safety of AWE operation. RDVDL employs a sparse Bayesian dictionary learning to preserve the dynamic mechanism information of AWE process which allows the easy interpretation of fault detection results. To improve the robustness to measurement uncertainty, a low-rank vector autoregressive (VAR) method is derived to reliably extract the serial correlation from process variables. The effectiveness of the proposed approach is demonstrated with an industrial hydrogen production process, and RDVDL can efficiently detect and diagnose critical AWE faults.