基于双权重多邻域保持嵌入的间歇过程故障检测
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兰州理工大学电信学院

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中图分类号:

TP277

基金项目:

国家自然科学基金资助项目(61763029,61873116, 61763027),国防基础科研项目(JCKY2018427C002),甘肃省高等学校产业支撑引导项目(2019C-05)和甘肃省工业过程先进控制重点实验室开放基金项目(2019KFJJ01).


Fault Detection of Batch Process Based on Double Weight and Multiple Neighborhood Preserving Embedding
Author:
Affiliation:

College of electrical engineering and information engineering, Lanzhou University of Technology

Fund Project:

Project supported by the National Natural Science Foundation,China (No.61763029, 61873116,61763027), the National Defense Basic Research Project of China (JCKY2018427C002), the Industrial support and guidance project of colleges and universities of Gansu Province (No. 2019C-05) and the open fund project of Key Laboratory of Gansu Advanced Control for Industrial Processes (No. 2019KFJJ05).

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    摘要:

    针对间歇过程数据的动态特性带来的故障检测问题,提出了一种双权重多邻域保持嵌入(Double Weight Multiple Neighborhood Preserving Embedding, DWMNPE)算法。首先,为每个样本点寻找时间近邻来描述样本点之间的时序相关关系;接着定义角度近邻,并为样本点寻找角度近邻和距离近邻,来表征样本点在空间上的相似性,通过提取这三种不同的流形特征,充分表征数据的局部结构特征;进一步构造一种新的目标函数,在考虑误差最小的同时兼顾三种近邻的顺序信息,可防止NPE算法在计算重构权值时丢失近邻顺序信息,在解决数据动态性的同时充分提取了原始数据的本质局部结构;最后对降维数据构造局部离群因子(Local Outlier Factor,LOF)统计量进行监控,消除数据非高斯特性对监控效果的不利影响。数值例子和青霉素发酵过程仿真结果验证了DWMNPE方法对动态性间歇过程故障检测的有效性。

    Abstract:

    Aiming at the problem of fault detection caused by the dynamic characteristic of batch process data, a double weight multiple neighborhood preserving embedding (DWMNPE) algorithm is proposed. Firstly, by finding time neighbors for each sample point, the time correlations between samples are reflected. By defining angle neighbors, sample points are reconstructed to represent the similarity in the space by finding time neighbors, angle neighbors and distance neighbors for sample points. Three different manifold features can fully extract the essential structure of original data. Then, considering the minimum error and three kinds of neighbor order information, a new objective function is constructed to further prevent the loss of neighbor order information when the reconstructing weights are calculated by using NPE algorithm. The data dynamic is solved, meanwhile, the essential local structure is achieved. Finally, the LOF statistic of the dimensionality reduction data is constructed to monitor the process and eliminate the bad influence of data non-Gaussian for monitoring effect. The results of a numerical example and the penicillin fermentation process simulation demonstrate that DWMNPE algorithm is effective for fault detection in dynamic batch process.

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  • 收稿日期:2020-05-29
  • 最后修改日期:2020-09-03
  • 录用日期:2020-09-08
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