A comparison and evaluation of key performance indicator-based multivariate statistics process monitoring approaches

作者：Zhang Kai^*; Hao Haiyang; Chen Zhiwen; Ding Steven X; Peng Kaixiang

来源：Journal of Process Control, 2015, 33: 112-126.

DOI：10.1016/j.jprocont.2015.06.007

• Key performance indicators
• Process monitoring
• Multivariate statistics
• Fault detection
• Expected detection delay

In this paper, the key performance indicator (KPI)-based multivariate statistical process monitoring and fault diagnosis (PM-FD) methods for linear static processes are surveyed and evaluated using the multivariate statistics framework. Based on their computational characteristics, the possible methods will be broadly classified into three categories: direct, linear regression-based, and PLS-based. The three categories are respectively presented in the first part, then the comparison study in aspects of their interconnections, geometric properties, and computational costs are shown, and finally their performance for PM-FD of KPIs is evaluated using a new evaluation index called expected detection delays where a numerical case and the Tennessee Eastman process are used to provide a demonstration of the evaluation result.

• 出版日期2015-9
• 单位北京科技大学