Dynamic fault detection and diagnosis of industrial alkaline water
  electrolyzer process with variational Bayesian dictionary learning
Qi Zhang Lei Xie Weihua Xu Hongye Su
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.