根本原因
根本原因分析
过程(计算)
计算机科学
词根(语言学)
断层(地质)
质量(理念)
故障检测与隔离
数据挖掘
产品(数学)
可靠性工程
主成分分析
风险分析(工程)
人工智能
工程类
业务
数学
地质学
哲学
操作系统
地震学
认识论
执行机构
语言学
几何学
作者
Gang Li,S. Joe Qin,Yuan Tao
标识
DOI:10.1016/j.chemolab.2016.09.006
摘要
Data driven fault detection and diagnosis methods become more and more attractive in modern industries especially process industries. They can not only guarantee safe operation but also greatly improve product quality. For example, dynamic principal component analysis models and reconstruction based contribution are widely applicable in many occasions. However, there is one issue which does not receive enough attention, namely locating the root cause of a fault when it occurs. In this paper, a framework of root cause location is proposed to address this issue, including both stationary faults and nonstationary faults. A case study on Tennessee Eastman process is used to demonstrate the usage and effectiveness of these approaches. Results show the proposed framework is valid.
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