Dynamic fault detection and diagnosis for alkaline water electrolyzer with variational Bayesian Sparse principal component analysis

TL;DR

This paper introduces a novel variational Bayesian sparse PCA method for reliable fault detection and diagnosis in alkaline water electrolyzers, addressing noise challenges in industrial process monitoring.

Contribution

It develops VBSPCA with Gaussian and Laplace priors for enhanced fault detection in noisy electrolyzer data, integrating sparse autoregression and fault reconstruction techniques.

Findings

Effective fault detection in industrial electrolyzer data

Both Gaussian and Laplace prior VBSPCA outperform traditional methods

Successful application in real hydrogen production process

Abstract

Electrolytic hydrogen production serves as not only a vital source of green hydrogen but also a key strategy for addressing renewable energy consumption challenges. For the safe production of hydrogen through alkaline water electrolyzer (AWE), dependable process monitoring technology is essential. However, random noise can easily contaminate the AWE process data collected in industrial settings, presenting new challenges for monitoring methods. In this study, we develop the variational Bayesian sparse principal component analysis (VBSPCA) method for process monitoring. VBSPCA methods based on Gaussian prior and Laplace prior are derived to obtain the sparsity of the projection matrix, which corresponds to $\ell_2$ regularization and $\ell_1$ regularization, respectively. The correlation of dynamic latent variables is then analyzed by sparse autoregression and fault variables are diagnosed by fault reconstruction. The effectiveness of the method is verified by an industrial hydrogen production process, and the test results demonstrated that both Gaussian prior and Laplace prior based VBSPCA can effectively detect and diagnose critical faults in AWEs.