Toward robust process monitoring of complex process industries based on denoising sparse auto-encoder

Robust industrial process monitoring is crucial to ensure production safety and product quality stability. How-ever, the process monitoring of high-dimensional, nonlinear, complex industrial processes is still challenging due to their inherent complexities, such as multi-phase, multi-field, and tight coupling of multiple sub-processes. In this article, a simple yet robust nonlinear process monitoring scheme based on a kind of denoising sparse auto-encoder (DSAE) is proposed. Specifically, a novel hybrid auto-encoder, namely DSAE, is established, to address the strong redundancy, nonlinearity and noise interference in process variables by integrating a sparse auto-encoder (SAE) and a denoising auto-encoder (DAE). Successively, an online process monitoring model is established by introducing two new process monitoring statistics computed on the DSAE-based feature repre-sentation space and residual space, respectively. Moreover, the Kernel density estimation (KDE) approach is adopted to determine the corresponding control limits of the monitoring statistics, which can avoid the con-ventional empirical assumption of F or Chi-square distribution on monitoring statistics. Extensive confirmative and comparative experiments conducted for the fault monitoring of a nonlinear numerical simulation system, the benchmark Tennessee Eastman process (TEP) and ventilation exhaust fans (VEFs) in the continuous casting process (CCP) from a top steel plant in China show that the proposed method is promising. Specifically, the proposed method performs favorably against the representative process monitoring methods, and it has espe-cially a stronger robustness against noise and uncertain interference in nonlinear process systems.

Automated summary

This article proposes a robust nonlinear process monitoring scheme based on a denoising sparse auto-encoder (DSAE). The scheme addresses the challenges posed by high-dimensional, nonlinear, and complex industrial processes. The proposed method demonstrates promising results in fault monitoring experiments.