Operation performance assessment for multimode processes based on GMM and Bayesian inference
ZOU Xiao-yu
CHANG Yu-qing
WANG Fu-li
ZHOU Yang
Abstract：To maximize the comprehensive economic benefits of enterprises, the production process ought to be kept in the optimal operating performance grade. To solve the problem of process state judgement for multimode processes, a novel operation performance assessing approach is proposed in this paper. One Gaussian mixture model (GMM) is established for a same running grade with multi modes in this article, ensuring the precision of feature extraction and avoiding mode division. As to online evaluation strategy, Bayesian inference is applied to calculate the Posterior probability of the current performance belonging to each grade. Sliding window is then introduced to help determine the running state. The proposed method turns to be an effective solution to the multi-modal process operating performance optimality online assessment. A novel variable contribution calculation technique is subsequently put forward, in the form of partial derivatives, which is successfully applied to cause identification when the performance is assessed to be non-optimal. Finally the validity of the proposed approach is illustrated through TE process.
Keywords：multimode processoperating performance assessmentnonoptimal cause identificationGaussian mixture model (GMM)Bayesian inference
Publication Date：2016-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：8( 164-171 )