计算机科学
过程(计算)
主成分分析
水准点(测量)
数据挖掘
统计的
故障检测与隔离
集成学习
聚类分析
贝叶斯推理
机器学习
人工智能
特征(语言学)
贝叶斯概率
数学
统计
操作系统
哲学
语言学
执行机构
地理
大地测量学
作者
Huifen Hong,Chao Jiang,Xin Peng,Weimin Zhong
标识
DOI:10.1021/acs.iecr.9b05547
摘要
Slow feature analysis (SFA) has been extensively adopted for process monitoring. Since the prominent ability of exploring dynamic information of the industrial process, SFA could monitor the process static and dynamic deviations concurrently. However, for complex and large-scale processes, it is difficult for a single SFA model to monitor the whole process well because of the complex relationship within massive volumes of variables. To address this issue and get a better monitoring performance, a novel ensemble process monitoring method based on slow feature analysis models is proposed as ensemble SFA (ESFA) in this paper. The proposed method develops a set of SFA models based on different combinations of variables, and the divisive hierarchical clustering algorithm (DHCA) is performed to pick out some models with great diversity as the base learners. Then, the fault detection results of base models would be combined into a comprehensive indicator through Bayesian inference. Furthermore, the ESFA method also provides an ES2 statistic for monitoring process dynamics to differentiate the deviations of normal operating condition changes from dynamic anomalies incurred by real faults. Finally, compared with basic SFA and several principal component analysis (PCA)-based methods, the validity of the proposed method is demonstrated through the case studies of the Tennessee Eastman (TE) benchmark process and the BSM1 process.
科研通智能强力驱动
Strongly Powered by AbleSci AI