Variational Bayesian Sparse Principal Component Analysis for Fault Detection and Diagnosis in Alkaline Water Electrolyzers

Variational Bayesian sparse principal component analysis (VBSPCA) methods based on Gaussian and Laplace priors are developed to effectively detect and diagnose critical faults in alkaline water electrolyzers by exploiting the dynamic correlation of latent variables and reducing the impact of noise.