故障检测与隔离
计算机科学
水准点(测量)
极小极大
相关向量机
维数之咒
线性判别分析
支持向量机
假警报
人工智能
模式识别(心理学)
算法
数学优化
数学
大地测量学
执行机构
地理
作者
Chen Yang,Yan Li,Qijun Chen
出处
期刊:IEEE Transactions on Industrial Informatics
[Institute of Electrical and Electronics Engineers]
日期:2023-03-01
卷期号:19 (3): 3198-3207
被引量:2
标识
DOI:10.1109/tii.2022.3182002
摘要
This article presents a novel two-stage fault-detection (FD) method composed of a preclassifier and a reclassifier for complex industrial processes, where the preclassifier is developed by combining linear discriminant analysis and minimax probability machine to reduce dimensionality and classify fully separable data with low computation time. For overlapping data that cannot be separated by the preclassifier, a reclassifier is designed by constructing a constrained relevance vector machine (RVM), according to Neyman–Pearson principle, to decrease the missed alarm rate. The reclassifier has a lower computational load than traditional RVM due to the amount and dimensionality of reclassified data reduced by the first stage, thereby a balance between detection accuracy and computational burden of the whole FD method can be achieved. Finally, an industrial benchmark of Tennessee–Eastman process is utilized to verify the effectiveness of the proposed FD method.
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