故障检测与隔离
过程(计算)
潜变量
卡尔曼滤波器
计算机科学
采样(信号处理)
马尔可夫过程
对偶(语法数字)
断层(地质)
控制理论(社会学)
人工智能
滤波器(信号处理)
数学
统计
艺术
文学类
地震学
执行机构
计算机视觉
地质学
操作系统
控制(管理)
作者
Yuchen He,Ze Ying,Yun Wang,Jie Wang
出处
期刊:IEEE Transactions on Instrumentation and Measurement
[Institute of Electrical and Electronics Engineers]
日期:2023-01-01
卷期号:72: 1-8
被引量:1
标识
DOI:10.1109/tim.2022.3180436
摘要
Quality-related process monitoring is an important tool to ensure process safety and product quality. However, the existence of process dynamics and multirate sampling makes it difficult to construct an efficient monitoring model. In order to handle process dynamics and multirate sampling, a multirate process monitoring method based on a dynamic dual-latent variable model is proposed. The model involves two sets of latent variables modeled as first-order Markov chains, which are used to capture both quality-related and quality-unrelated dynamic information. In addition, to deal with multiple sampling rates in the process data, the proposed model is combined with a multirate Kalman filtering technique. An expectation-maximization (EM) algorithm is used to estimate the unknown parameters, and a fault detection strategy is developed. The higher fault detection rate of the proposed method is verified by two application studies including a real industrial experiment and the Tennessee Eastman (TE) process.
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