Review of Quality-related Fault Detection and Diagnosis Techniques for Complex Industrial Processes
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摘要: 质量相关的故障检测与诊断技术是保证安全生产及获得可靠产品质量的有效手段，是当前国际过程控制领域的研究热点.首先，梳理了质量相关的故障检测技术中典型方法的基本思想和改进过程；其次，概述了质量相关的故障诊断技术中常用的贡献图法及其相关改进方法之间的联系，并通过带钢热连轧过程（Hot strip mill process，HSMP）案例比较了各种典型方法在质量相关的故障检测与诊断性能上的异同；最后，面向复杂工业过程运行数据的主要特性，评析了质量相关的故障检测与诊断方法的研究现状，并指出了该研究领域亟需解决的问题和未来的发展方向.Abstract: Quality-related fault detection and diagnosis techniques have been extensively applied to the process control field to guarantee production safety and product quality, which, thus, have recently become an active area of research both in academia and industry. Firstly, the basic idea and improvements of typical methods for quality-related fault detection techniques are introduced in this paper. Then, quality-related fault diagnosis techniques are revisited, with special case study attention on the contribution plot based methods and their improved methods, in which on a hot strip mill process (HSMP) is used to show their different performances. Finally, the state-of-the-art research of quality-related fault detection and diagnosis methods for main characteristics of complex industrial process operation data are reviewed, and open problems, challenges and perspectives for future research are presented as well.
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Key words:
- Quality-related /
- fault detection /
- fault diagnosis /
- partial least squares (PLS) /
- contribution plot
1) 本文责任编委 胡昌华 -
表 1 对比结果
Table 1 Comparison results
方法 计算复杂度 投影结构 子空间个数 统计量个数 PLS $a$次SVDs 斜交 2 2 T-PLS $a+3$次SVDs 正交 4 4 C-PLS $a+2$次SVDs 正交 3 3 M-PLS 2次SVDs 正交 2 2 E-PLS 3次SVDs 正交 3 3 PCR 3次SVDs 正交 3 3 CVA 1次SVD 正交 2 2 
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表 2 监测统计量总结
Table 2 Summary of monitoring statistics
方法 质量相关故障统计量 质量无关故障统计量 PLS $T^{2}$ Q T-PLS $T_{y}^{2}$和$Q_{r}$ $T_{o}^{2}$和$T_{r}^{2}$ C-PLS $T_{c}^{2}$ $T_{x}^{2}$和$Q_{x}$ M-PLS $T_{y}^{2}$ $T_{x}^{2}$ E-PLS $T_{y}^{2}$ $T_{x}^{2}$和$Q_{x}$ PCR $T_{y}^{2}$ $T_{o}^{2}$和$Q_{o}$ CVA $T_{p}^{2}$ $T_{q}^{2}$
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表 3 过程及质量变量分配表
Table 3 Assignment table of process and quality variables
变量 类型 描述 单位 1~7 过程变量 第$i$机架的平均辊缝 ($i=1,cdots,7$) mm 8~14 过程变量 第$i$机架的轧制力 ($i=1,cdots,7$) MN 15~20 过程变量 第$i$机架的弯辊力 ($i=2,cdots,7$) MN 21 质量变量 精轧末机架出口厚度 mm
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表 4 故障报警率及故障检测率对比结果
Table 4 FAR and FDR comparison results
方法 质量相关
FAR质量相关
FDR质量无关
FAR质量无关
FDRPLS 0.1812 0.8680 0.0019 0.9960 T-PLS 0.2146 0.8920 0.0078 0.9981 C-PLS 0.2181 0.9713 0.0041 0.9982 M-PLS 0.1946 0.8480 0.0530 0.9981 E-PLS 0.2179 0.9712 0.0041 0.9983 PCR 0.1927 0.9014 0.0052 0.9944 CVA 0.2011 0.8966 0.0037 0.9929
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