基于改进的局部近邻标准化和kNN的多阶段过程故障检测

doi:10.11772/j.issn.1001-9081.2017112701

计算机应用 ›› 2018, Vol. 38 ›› Issue (7): 2130-2135.DOI: 10.11772/j.issn.1001-9081.2017112701

• 应用前言、交叉与综合 • 上一篇下一篇

基于改进的局部近邻标准化和kNN的多阶段过程故障检测

冯立伟^1,2, 张成^1,2, 李元¹, 谢彦红^1,2

1. 沈阳化工大学数理系, 沈阳 110142;
2. 沈阳化工大学过程故障诊断研究中心, 沈阳 110142

收稿日期:2017-11-15 修回日期:2017-12-30 出版日期:2018-07-10 发布日期:2018-07-12
通讯作者: 李元
作者简介: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;张成(1979-),男,辽宁沈阳人,副教授,硕士,主要研究方向:过程故障诊断分析;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;谢彦红(1964-),女,辽宁沈阳人,教授,博士,主要研究方向:过程控制与故障诊断。
基金资助:
国家自然科学基金重点基金资助项目（61490701，61673279）；辽宁省教育厅基金资助一般项目（L2015432）；辽宁省自然科学基金资助项目（2015020164）。

Fault detection for multistage process based on improved local neighborhood standardization and kNN

FENG Liwei^1,2, ZHANG Cheng^1,2, LI Yuan¹, XIE Yanhong^1,2

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:2017-11-15 Revised:2017-12-30 Online:2018-07-10 Published:2018-07-12
Supported by:
This work is partially supported by the National Natural Science Foundation of China (61490701, 61673279), the Project of Education Department in Liaoning Province (L2015432), the Natural Science Foundation of Liaoning Province (2015020164).

摘要/Abstract

摘要： 针对多阶段过程数据具有多中心和各工序结构不同的特征问题，提出了一种基于改进的局部近邻标准化和k近邻的故障检测（ILNS-kNN）方法。首先寻找样本的前k个近邻样本的前K局部近邻集；其次使用局部近邻集的均值和标准差来标准化样本，获得标准样本；最后在标准样本集上计算样本的累积近邻距离作为检测指标进行故障检测。改进的局部近邻标准化（ILNS）将各阶段数据的中心平移到原点，并且调整各阶段数据的离散程度，使之近似相等，从而将多阶段过程数据融合为服从单一多元高斯分布的单阶段数据。进行了青霉素发酵过程故障检测实验。实验结果表明ILNS-kNN方法对所设置的六类故障的检测率高于97%。ILNS-kNN方法在保持对一般多阶段过程故障的检测能力的同时，能够实现对阶段方差差异显著的多阶段过程故障的检测，从而更好地保证多阶段生产过程的安全性和产品的高质量。

关键词: 标准化, 多阶段过程, k近邻, 故障检测, 主元分析

Abstract: Concerning the problem that multistage process data has the characteristics of multi-center and different process structure, a fault detection based on Improved Local Neighborhood Standardization and k Nearest Neighbors (ILNS-kNN) method was proposed. Firstly, K local neighbor set of k neighbors of the sample was found. Secondly, the sample was standardized to obtain the standard sample by using mean and standard deviation of K local neighbor set. Finally, fault detection was carried out by calculating the cumulative neighbor distance of samples in the standard sample set. The center of each stage data was shifted to the origin by Improved Local Neighborhood Standardization (ILNS), and dispersion degree of each stage data was adjusted approximately to the same, then the multistage process data was fused to single stage data obeying multivariate Gauss distribution. The fault detection of penicillin fermentation process experiment was carried out. The experimental results show that the ILNS-kNN method has more than 97% detection rate for six types of faults. The ILNS-kNN method can detect faults not only in general multistage process, but also in multistage process with significant different variances. It is better to ensure the safety of multistage process and the high quality of product.

Key words: standardization, multistage process, k Nearest Neighbor (kNN), fault detection, Principal Component Analysis (PCA)

中图分类号:

TP277

冯立伟, 张成, 李元, 谢彦红. 基于改进的局部近邻标准化和kNN的多阶段过程故障检测[J]. 计算机应用, 2018, 38(7): 2130-2135.

FENG Liwei, ZHANG Cheng, LI Yuan, XIE Yanhong. Fault detection for multistage process based on improved local neighborhood standardization and kNN[J]. Journal of Computer Applications, 2018, 38(7): 2130-2135.

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基于改进的局部近邻标准化和kNN的多阶段过程故障检测

Fault detection for multistage process based on improved local neighborhood standardization and kNN

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