基于多重局部重构模型的连续过程故障诊断

赵春晖; 李文卿; 孙优贤; 高福荣

doi:10.3724/SP.J.1004.2013.00487

基于多重局部重构模型的连续过程故障诊断

doi: 10.3724/SP.J.1004.2013.00487

1.
浙江大学控制系国家工业控制重点实验室杭州 310027

详细信息

通讯作者:
赵春晖

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- 被引次数: 0
出版历程
- 收稿日期: 2012-03-17
- 修回日期: 2012-11-06
- 刊出日期: 2013-05-20

Multiple Local Reconstruction Model-based Fault Diagnosis for Continuous Processes

1.
State Key Laboratory of Industrial Control Technology, Department of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China

摘要

摘要: 为了提高故障诊断性能, 本文对故障特征随时间发展变化的多样性进行了探讨分析. 本文揭示了故障过程呈现时变特性, 即故障过程在不同时段反映出不同的变量相关性, 提出了一种故障时段划分算法. 该算法将故障划分为不同时段, 在每一个时段中, 故障特征被认为是基本类似的. 在此基础上, 针对不同时段建立了不同的故障分解模型, 并揭示了不同故障状态与正常状态的关系. 通过划分不同故障特征, 可以区分不同的故障特征, 建立更精确的重构模型. 该方法很好地阐述了故障的演变行为特征, 能够更精确地进行故障重构从而确定故障原因. 通过在田纳西伊士曼仿真过程上的应用验证了该方法的可行性及诊断性能.
- 主元分析, 故障特征多样性, 故障过程划分, 故障重构, 故障诊断
Abstract: In the present work, the multiplicity of fault characteristics is proposed and analyzed to improve the fault diagnosis performance. It is based on the following recognition that the underlying fault characteristics in general do not stay constant but will present changes along the time direction. That is, the fault process reveals different variable correlations across different time periods. To analyze the multiplicity of fault characteristics, a fault division algorithm is developed to divide the fault process into multiple local time periods where the fault characteristics are deemed similar within the same local time period. Then a representative fault decomposition model is built in each local time period to reveal the relationships between the fault and normal operation status. In this way, these different fault characteristics can be modeled respectively. The proposed method gives an interesting insight into the fault evolvement behaviors and a more accurate from-fault-to-normal reconstruction result can be expected for fault diagnosis. The feasibility and performance of the proposed fault diagnosis method are illustrated with the Tennessee Eastman process.
- Principal component analysis (PCA), multiplicity of fault characteristics, fault process division, fault reconstruction, fault diagnosis

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