基于局部近邻标准化和动态主元分析的故障检测策略

doi:10.11772/j.issn.1001-9081.2018010071

计算机应用 ›› 2018, Vol. 38 ›› Issue (9): 2730-2734.DOI: 10.11772/j.issn.1001-9081.2018010071

• 应用前沿、交叉与综合 • 上一篇下一篇

基于局部近邻标准化和动态主元分析的故障检测策略

张成^1,2, 郭青秀², 冯立伟^1,2, 李元²

1. 沈阳化工大学数理系, 沈阳 110142;
2. 沈阳化工大学技术过程故障诊断与安全性研究中心, 沈阳 110142

收稿日期:2018-01-10 修回日期:2018-04-10 出版日期:2018-09-10 发布日期:2018-09-06
通讯作者: 李元
作者简介:张成(1979—),男,辽宁沈阳人,副教授,博士研究生,主要研究方向:过程故障诊断分析;郭青秀(1992—),女,内蒙古呼和浩特人,硕士研究生,主要研究方向:过程故障检测;冯立伟(1980—),男,山东青州人,讲师,硕士,主要研究方向:过程故障检测;李元(1964—),女,辽宁沈阳人,教授,博士,主要研究方向:基于数据驱动复杂过程故障诊断。
基金资助:
国家自然科学基金资助项目（61490701，61673279）；辽宁省自然科学基金资助项目（2015020164）；辽宁省教育厅基金资助一般项目（L2015432）。

Fault detection strategy based on local neighbor standardization and dynamic principal component analysis

ZHANG Cheng^1,2, GUO Qingxiu², FENG Liwei^1,2, LI Yuan²

1. Department of Science, Shenyang University of Chemical Technology, Shenyang Liaoning 110142, China;
2. Research Center for Technical Process Fault Diagnosis and Safety, Shenyang University of Chemical Technology, Shenyang Liaoning 110142, China

Received:2018-01-10 Revised:2018-04-10 Online:2018-09-10 Published:2018-09-06
Contact: 李元
Supported by:
This work is partially supported by the National Natural Science Foundation of China (61490701, 61673279), the Natural Science Foundation of Liaoning Province (2015020164), the General Project of Liaoning Provincial Department of Education Fund (L2015432).

摘要/Abstract

摘要： 针对工业过程的动态和多模态特性，提出一种基于局部近邻标准化（LNS）和动态主元分析（DPCA）相结合的故障检测方法（LNS-DPCA）。首先，在训练数据集中寻找样本的K近邻集；然后，应用K近邻集的均值与标准差对当前样本进行标准化处理；最后，在新的数据集中应用DPCA方法确定T²和SPE控制限进行故障检测。LNS方法能够消除过程的多模态特征，使得标准化后数据近似服从多元高斯分布，且保持过程离群点偏离正常样本轨迹；而结合DPCA方法则能够提高对具有动态特性过程的监视性能。利用数值例子和青霉素发酵过程进行仿真，并将测试结果与主元分析法（PCA）、DPCA、K近邻故障检测（FD-KNN）等方法进行对比分析，验证了LNS-DPCA方法的有效性。

关键词: 局部近邻标准化, 动态主元分析, 多模态, 青霉素发酵过程, 故障检测

Abstract: Aiming at the processes with dynamic and multimode characteristics, a fault detection strategy based on Local Neighbor Standardization (LNS) and Dynamic Principal Component Analysis (DPCA) was proposed. First, the K nearest neighbors set of each sample in training data set was found, then the mean and standard deviation of each variable were calculated. Next, the above mean and standard deviation were applied to standardize the current samples. At last, the traditional DPCA was applied in the new data set to determine the control limits of T² and SPE statistics respectively for fault detection. LNS can eliminate the multimode characteristic of a process and make the new data set follow a multivariate Gaussian distribution; meanwhile, the feature of a outlier deviating from normal trajectory can also be maintained. LNS-DPCA can reduce the impact of multimode structure and improve the detectability of fault in processes with dynamic property. The efficiency of the proposed strategy was implemented in a simulated case and the penicillin fermentation process. The experimental results indicate that the proposed method outperforms the Principal Component Analysis (PCA), DPCA and Fault Detection based on K Nearest Neighbors (FD-KNN).

Key words: Local Neighbor Standardization (LNS), Dynamic Principal Component Analysis (DPCA), multimode, penicillin fermentation process, fault detection

中图分类号:

TP277

张成, 郭青秀, 冯立伟, 李元. 基于局部近邻标准化和动态主元分析的故障检测策略[J]. 计算机应用, 2018, 38(9): 2730-2734.

ZHANG Cheng, GUO Qingxiu, FENG Liwei, LI Yuan. Fault detection strategy based on local neighbor standardization and dynamic principal component analysis[J]. Journal of Computer Applications, 2018, 38(9): 2730-2734.

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基于局部近邻标准化和动态主元分析的故障检测策略

Fault detection strategy based on local neighbor standardization and dynamic principal component analysis

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