Monitoring of operating point and process dynamics via probabilistic slow feature analysis

作者：Guo, Feihong; Shang, Chao; Huang, Biao; Wang, Kangcheng; Yang, Fan^*; Huang, Dexian

来源：Chemometrics and Intelligent Laboratory Systems, 2016, 151: 115-125.

DOI：10.1016/j.chemolab.2015.12.017

• Latent variable model
• Process data analysis
• Missing data
• Process monitoring
• Alarm removal

Traditional multivariate statistical process monitoring (MSPM) approaches aim at detecting deviations from the routine operating condition. However, if the process remains well controlled by feedback controllers in spite of some deviations, alarms triggered in this context become no longer necessary. In this regard, slow feature analysis (SFA) has been recently applied to MSPM tasks by Shang et al. (2015), which allows for seperate distributions of both nominal operating points and dynamic behaviors. Since a poor control performance is always characterized by dynamics anomalies, one can discriminate nominal operating deviations with acceptable control performance, from real faults that deserve more attentions, according to the temporal dynamics of processes. In this work, we propose a new process monitoring scheme based upon probabilistic SFA (PSFA). Compared to deterministic SFA, its probabilistic extension takes the measurement noise into considerations and allows for missing data imputation conveniently, which is beneficial for process monitoring. Apart from generic T-2 and SPE metrics for monitoring the operating point, a novel S-2 statistics is considered for exclusively monitoring temporal behaviors of processes. Two case studies are provided to show the efficacy of the proposed monitoring approach.

• 出版日期2016-2-15
• 单位清华大学