Multimode process monitoring with Bayesian inference‐based finite Gaussian mixture models

Abstract

in Wiley InterScience (www.interscience.wiley.com).For complex industrial processes with multiple operating conditions, the traditional multivariate process monitoring techniques such as principal component analysis (PCA) and partial least squares (PLS) are ill-suited because the fundamental assumption that the operating data follow a unimodal Gaussian distribution usually becomes invalid. In this article, a novel multimode process monitoring approach based on finite Gaussian mixture model (FGMM) and Bayesian inference strategy is proposed. First, the process data are assumed to be from a number of different clusters, each of which corresponds to an operating mode and can be characterized by a Gaussian component. In the absence of a priori process knowledge, the Figueiredo-Jain (F-J) algorithm is then adopted to automatically optimize the number of Gaussian components and estimate their statistical distribution parameters. With the obtained FGMM, a Bayesian inference strategy is further utilized to compute the posterior probabilities of each monitored sample belonging to the multiple components and derive an integrated global probabilistic index for fault detection of multimode processes. The validity and effectiveness of the proposed monitoring approach are illustrated through three examples: (1) a simple multivariate linear system, (2) a simulated continuous stirred tank heater (CSTH) process, and (3) the Tennessee Eastman challenge problem. The comparison of monitoring results demonstrates that the proposed approach is superior to the conventional PCA method and can achieve accurate and early detection of various types of faults in multimode processes.

A branch and bound method for isolation of faulty variables through missing variable analysis

“…During the past few years, the focus of the research in MSPM has been on developing sophisticated statistical models to obtain more realistic representation of the process behaviour. Some notable progress has been made in dealing with dynamic processes [4][5][6][7][8] and process with non-Gaussian distributed data [8][9][10][11][12][13]. However, investigation into the "downstream" step of MSPM, that is, the diagnosis of the source or cause of the detected fault, has been relatively limited.…”

Statistical process monitoring based on improved principal component analysis and its application to chemical processes

“…TE process is a well-known benchmark for testing the performance of various fault detection methods (Lyman and Georgakist, 1995;Yu and Qin, 2008;Liu et al, 2010;Chen and Yan, 2012;Stubbs et al, 2012). A flowchart of the TE process is shown schematically in Fig.…”

Quality prediction for polypropylene production process based on CLGPR model

“…Besides, when some new operation mode is identified, the traditional mixture Gaussian process models need to be re-trained, demanding considerable computational resource and maintenance effort. Other similar techniques for multimode modeling include the Gaussian mixture model (GMM) approach and the fuzzy modeling method (Choi et al, 2004;Yu & Qin, 2008;Yu & Qin, 2009;Rong et al, 2006;Huang & Hahn, 2009). The GMM method can also gives a probabilistic model structure for different operation modes.…”