Nonlinear and robust statistical process monitoring based on variant autoencoders

• A novel nonlinear and robust process monitoring approach based on variant autoencoders (variant AEs) is developed.
• Proposed variant AEs monitoring models include denoising autoencoders model and contractive autoencoders model.
• New test statistics, that is statistic H2, is constructed based on the robust feature representations.
• Variant autoencoders monitoring is applied to the Tennessee Eastman process and have good process-monitoring performance.

Autoencoders (AEs) are an effective means for nonlinear feature extraction and dimension reduction. Variant autoencoders are an improvement over traditional AEs in terms of robustness. This paper proposes a novel nonlinear and robust process-monitoring approach based on variant autoencoders (variant AEs), which include denoising autoencoders (DAE) and contractive autoencoders (CAE). The CAE and DAE are powerful for extracting robust and nonlinear feature representations or manifold structures underlying data from industrial processes. Next, an online monitoring model is built through constructing new test statistic H2 based on the robust feature representations. The control limits are determined by kernel density estimation. The proposed method was applied to the Tennessee Eastman process (TE process) to evaluate its monitoring performance, and it demonstrated outstanding process-monitoring performance through the experimental results, especially for the barely detectable faults, such as 3, 5, 9, 10, 11, 15, 19, 20 and 21. Variant AEs monitoring provides a simple and very effective process-monitoring method for industrial processes.