基于增量式等距映射同双重局部密度方法的工业过程故障检测
收稿日期: 2022-10-28
修回日期: 2022-12-09
录用日期: 2022-12-30
网络出版日期: 2024-04-30
基金资助
国家自然科学基金(61673279);国家自然科学基金(62273242)
Industrial Process Fault Detection Based on Incremental Isometric Mapping and Double Local Density Method
Received date: 2022-10-28
Revised date: 2022-12-09
Accepted date: 2022-12-30
Online published: 2024-04-30
针对工业过程的非线性和动态性问题,提出一种基于流形学习下的增量式等距映射(IISOMAP)与双重局部密度(DLD)相结合的故障检测方法(IISOMAP-DLD).利用 IISOMAP 将原始数据映射到低维流形特征子空间和剩余子空间;然后,在两个子空间中分别引入双重局部密度方法构建统计量对过程进行监控;最后,将IISOMAP-DLD方法应用到田纳西-伊斯曼(TE)过程.实验结果表明,IISOMAP-DLD对比其他方法有更高的故障检测率.IISOMAP在保留数据内在特征的同时,解决了过程的非线性问题,而双重局部密度方法可消除过程的动态性.
冯立伟, 孙立文, 顾欢, 李元 . 基于增量式等距映射同双重局部密度方法的工业过程故障检测[J]. 上海交通大学学报, 2024 , 58(4) : 525 -533 . DOI: 10.16183/j.cnki.jsjtu.2022.423
To address the nonlinearity and dynamics of industrial processes, an incremental isometric mapping (IISOMAP) in combination with double local density (DLD) is proposed as a fault detection method (IISOMAP-DLD) based on stream shape learning. First, IISOMAP is used to map the raw data into a low-dimensional manifold feature subspace and a residual subspace. Then, the double local density method is introduced in the two subspaces respectively to construct statistics to monitor the process. Finally, the IISOMAP-DLD method is applied to the Tennessee-Eastman (TE) process, and the experimental results show that IISOMAP-DLD has a higher fault detection rate than the other methods. IISOMAP preserves the intrinsic characteristics of the data and solves the nonlinear problems of the process, while the double local density method can eliminate the dynamic of the process.
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