| 基于T-PLS贡献图方法的故障诊断技术 |
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| 引用本文： | 李钢,秦洒钊,吉吟东,周东华.基于T-PLS贡献图方法的故障诊断技术[J].自动化学报,2009,35(6):759-765. |
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| 作者姓名： | 李钢 秦洒钊 吉吟东 周东华 |
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| 作者单位： | 1.清华大学自动化系 中国北京 100084 |
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| 基金项目： | 国家重点基础研究发展规划(973计划)，国家自然科学基金，Changjiang Professorship by Ministry of Education of P. R. China |
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| 摘 要： | 多变量统计过程监控对于复杂工业过程是一种有效的故障检测和诊断技术. 最小二乘(或称潜空间投影)模型是多变量统计过程监控中常用的一种投影模型, 能够同时对过程数据和质量数据进行建模. 讨论了一种新的基于全潜空间投影模型的故障诊断技术. 全潜空间投影模型中有4个检测统计量. 提出了一种新的T2贡献图计算方法, 对于所有检测统计量, 得到了相应的贡献图算法. 为了确定一个变量是否发生了故障, 计算所有变量贡献图的控制限. 该技术可以将辨识到的故障变量分为与Y有关和与Y无关的两类. 基于Tennessee Eastman过程的案例研究表明了该技术的有效性.
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| 关 键 词： | 数据驱动 全潜空间投影 贡献图 故障诊断 |
| 收稿时间： | 2008-12-11 |
| 修稿时间： | 2009-2-24 |
Total PLS Based Contribution Plots for Fault Diagnosis |
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| LI Gang QIN Si-Zhao JI Yin-Dong ZHOU Dong-Hua.Total PLS Based Contribution Plots for Fault Diagnosis[J].Acta Automatica Sinica,2009,35(6):759-765. |
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| Authors: | LI Gang QIN Si-Zhao JI Yin-Dong ZHOU Dong-Hua |
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| Affiliation: | 1.Department of Automation, Tsinghua National Laboratory for Information Science and Technology, Tsinghua University, Beijing 100084, P.R. China;2.Departments of Chemical and Electrical Engineering, Viterbi School of Engineering, University of Southern California, California 90089, USA;3.Research Institute of Information Technology, Tsinghua University, Beijing 100084, P.R. China |
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| Abstract: | Multivariate statistical process monitoring (MSPM) is an efficient data-driven fault detection and diagnosis approach for complex industrial processes. Partial least squares or projection to latent structures (PLS) is one of the latent projection structures used in MSPM, which uses process data X and quality data Y together. In this paper, we discuss a new fault diagnosis approach based on total projection to latent structures (T-PLS). Four kinds of monitoring statistics are used in T-PLS, and a new definition of variable contributions to T2 of PLS is proposed. Then, definitions of variable contributions to all statistics are derived to identify the faults. Control limits for contribution plots are calculated to identify whether a variable is in abnormal situation or not. Further, the proposed method separates the identified variables into faulty variables related to Y and unrelated to Y more clearly than conventional method. A case study on Tennessee Eastman process (TEP) indicates the efficiency of the proposed approach. |
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| Keywords: | Data-driven total projection to latent structures (T-PLS) contribution plots fault diagnosis |
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