基于贡献率法的非线性工业过程在线故障诊断

彭开香; 张凯; 李钢

doi:10.3724/SP.J.1004.2014.00423

基于贡献率法的非线性工业过程在线故障诊断

doi: 10.3724/SP.J.1004.2014.00423

彭开香¹,
张凯^1, ,,
李钢²

1.
北京科技大学自动化学院北京 100083;
2.
清华大学自动化系北京 100084

基金项目:

Supported by National Natural Science Foundation of China (61074085), Beijing Natural Science Foundation (4122029, 4142035), and the Fundamental Research Funds for the Central Universities (FRF-SD-12-008B, FRF-AS-11-004B)

详细信息

通讯作者:
张凯

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- 被引次数: 0
出版历程
- 收稿日期: 2012-05-29
- 修回日期: 2013-08-23
- 刊出日期: 2014-03-20

Online Contribution Rate Based Fault Diagnosis for Nonlinear Industrial Processes

1.
Key Laboratory for Advanced Control of Iron and Steel Process, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China;
2.
Department of Automation, Tsinghua National Laboratory for Information Science and Technology, Tsinghua University, Beijing 100084, China

Funds:

摘要

摘要: 在过去几十年，核主成分分析（KPCA）已经广泛应用在数据驱动的过程监测领域. 大量的应用案例显示该算法简单、易用且有效. 然而，核函数的引入使得KPCA不能直接利用传统的贡献图方法进行故障诊断. 本文在重新审视和分析现有KPCA相关诊断方法的基础上，提出了一类新的贡献率方法，该方法能较清晰地解释故障变量. 在此基础上，建立了一套面向非线性在线故障诊断的框架. 最后，将该诊断框架应用到CSTR过程，结果显示该方法较传统的线性方法更有效.
- 核主成分分析 /
- 非线性 /
- 故障检测 /
- 贡献率 /
- 故障诊断
Abstract: Over past decades, kernel principal component analysis (KPCA) appeared quite popularly in data-driven process monitoring area. Enormous work has been done to show its simplicity, feasibility, and effectiveness. However, the introduction of kernel trick makes it impossible to directly employ traditional contribution plots for fault diagnosis. In this paper, on the basis of revisiting and analyzing the existing KPCA-relevant diagnosis approaches, a new contribution rate based method is proposed which can explain the faulty variables clearly. Furthermore, a scheme for online nonlinear diagnosis is established. In the end, a case study on continuous stirred tank reactor (CSTR) benchmark is applied to access the effectiveness of the new methodology, where the comparisons with the traditional linear method are involved as well.
- Kernel principal component analysis (KPCA) /
- nonlinear /
- fault detection /
- contribution rate /
- fault diagnosis

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参考文献(27)

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