Fault Detection of Complex Processes Using nonlinear Mean Function Based Gaussian Process Regression: Application to the Tennessee Eastman Process

Process monitoring or fault detection and diagnosis have gained tremendous attention over the past decade in order to achieve better product quality, minimise downtime and maximise profit in process industries. Among various process monitoring techniques, data-based machine learning approaches have become immensely popular in the past decade. However, a promising machine learning technique Gaussian process regression has not yet received adequate attention for process monitoring. In this work, Gaussian process regression (GPR)-based process monitoring approach is applied to the benchmark Tennessee Eastman challenge problem. Effect of various GPR hyper-parameters on monitoring efficiency is also thoroughly investigated. The results of GPR model is found to be better than many other techniques which is reported in a comparative study in this work.

Automated summary

In the past decade, data-driven machine learning techniques have become popular in process monitoring, with Gaussian process regression as a promising but underexplored method. This research applied GPR to the Tennessee Eastman challenge problem and found its performance to be superior to other techniques in a comparative study.