文章基本信息

标题：An Efficient Quality-Related Fault Diagnosis Method for Real-Time Multimode Industrial Process
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作者：Kaixiang Peng ; Bingzheng Wang ; Jie Dong 等
期刊名称：Journal of Control Science and Engineering
印刷版ISSN：1687-5249
电子版ISSN：1687-5257
出版年度：2017
卷号：2017
DOI：10.1155/2017/9560206
出版社：Hindawi Publishing Corporation
摘要：Focusing on quality-related complex industrial process performance monitoring, a novel multimode process monitoring method is proposed in this paper. Firstly, principal component space clustering is implemented under the guidance of quality variables. Through extraction of model tags, clustering information of original training data can be acquired. Secondly, according to multimode characteristics of process data, the monitoring model integrated Gaussian mixture model with total projection to latent structures is effective after building the covariance description form. The multimode total projection to latent structures (MTPLS) model is the foundation of problem solving about quality-related monitoring for multimode processes. Then, a comprehensive statistics index is defined which is based on the posterior probability of the monitored samples belonging to each Gaussian component in the Bayesian theory. After that, a combined index is constructed for process monitoring. Finally, motivated by the application of traditional contribution plot in fault diagnosis, a gradient contribution rate is applied for analyzing the variation of variable contribution rate along samples. Our method can ensure the implementation of online fault monitoring and diagnosis for multimode processes. Performances of the whole proposed scheme are verified in a real industrial, hot strip mill process (HSMP) compared with some existing methods.