过程(计算)
计算机科学
贝叶斯概率
可靠性(半导体)
贝叶斯推理
质量(理念)
数据挖掘
机器学习
可靠性工程
人工智能
工程类
操作系统
物理
哲学
认识论
功率(物理)
量子力学
作者
Shima Khatibisepehr,Biao Huang,Swanand Khare
标识
DOI:10.1016/j.jprocont.2013.05.007
摘要
In many industrial plants, development and implementation of advanced monitoring and control techniques require real-time measurement of process quality variables. However, on-line acquisition of such data may involve difficulties due to inadequacy of measurement techniques or low reliability of measuring devices. To overcome the shortcomings of traditional instrumentation, inferential sensors have been designed to infer process quality indicators from real-time measurable process variables. In recent years, due to the demonstrated advantages of Bayesian methods, interest in investigating the application of these methods for design of inferential sensors has grown. However, the potential of Bayesian methods for inferential modeling practices in the process industry has not yet been fully realized. This paper provides a general introduction to the main steps involved in development and implementation of industrial inferential sensors, and presents an overview of the relevant Bayesian methods for inferential modeling.
科研通智能强力驱动
Strongly Powered by AbleSci AI