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

doi:10.11772/j.issn.1001-9081.2017112701

Journal of Computer Applications ›› 2018, Vol. 38 ›› Issue (7): 2130-2135.DOI: 10.11772/j.issn.1001-9081.2017112701

Previous Articles Next Articles

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).

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

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）。

Abstract

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)

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

CLC Number:

TP277

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.

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.

References

[1] WISE B M, GALLAGHER N B, BUTLER S W, et al. A comparison of principal component analysis, multiway principal component analysis, trilinear decomposition and parallel factor analysis for fault detection in a semiconductor etch process[J]. Journal of Chemometrics, 1999, 13(3/4):379-396.
[2] CHERRY G A, QIN S J. Multiblock principal component analysis based on a combined index for semiconductor fault detection and diagnosis[J]. IEEE Transactions on Semiconductor Manufacturing, 2006, 19(2):159-172.
[3] GE Z, SONG Z. Semiconductor manufacturing process monitoring based on adaptive substatistical PCA[J]. IEEE Transactions on Semiconductor Manufacturing, 2010, 23(1):99-108.
[4] KRESTA J V, MACGREGOR J F, MARLIN T E. Multivariate statistical monitoring of process operating performance[J]. Canadian Journal of Chemical Engineering, 1991, 69(1):35-47.
[5] ZHAO S J, ZHANG J, XU Y M. Performance monitoring of processes with multiple operating modes through multiple PLS models[J]. Journal of Process Control, 2006, 16(7):763-772.
[6] KANO M, TANAKA S, HASEBE S, et al. Monitoring independent components for fault detection[J]. Aiche Journal, 2003, 49(4):969-976.
[7] GE Z, SONG Z. Performance-driven ensemble learning ICA model for improved non-Gaussian process monitoring[J]. Chemometrics & Intelligent Laboratory Systems, 2013, 123(2):1-8.
[8] GE Z, GAO F, SONG Z. Two-dimensional Bayesian monitoring method for nonlinear multimode processes[J]. Chemical Engineering Science, 2011, 66(21):5173-5183.
[9] TEPPOLA P, MUJUNEN S P, MINKKINEN P. Adaptive fuzzy C-means clustering in process monitoring[J]. Chemometrics & Intelligent Laboratory Systems, 1999, 45(1/2):23-38.
[10] YU J, QIN S J. Multimode process monitoring with Bayesian inference-based finite Gaussian mixture models[J]. Aiche Journal, 2008, 54(7):1811-1829.
[11] YU J, QIN S J. Multiway gaussian mixture model based multiphase batch process monitoring[J]. Industrial & Engineering Chemistry Research, 2009, 48(18):8585-8594.
[12] 张成,李秀玉,逄玉俊,等.基于GMM的马氏距离kNN故障检测方法研究[J].测控技术,2014,33(9):13-17.(ZHANG C, LI X Y, PANG Y J, et al. Mahalanobis distance kNN fault detection method based on Gaussian mixture model[J]. Measurement & Control Technology, 2014, 33(9):13-17.)
[13] HWANG D H, HAN C. Real-time monitoring for a process with multiple operating modes[J]. Control Engineering Practice, 1999, 7(7):891-902.
[14] LANE S, MARTIN E B, KOOIJMANS R, et al. Performance monitoring of a multi-product semi-batch process[J]. Journal of Process Control, 2001, 11(1):1-11.
[15] HE Q P, WANG J. Fault detection using the k-nearest neighbor rule for semiconductor manufacturing processes[J]. IEEE Transactions on Semiconductor Manufacturing, 2007, 20(4):345-354.
[16] ZHOU Z, WEN C, YANG C. Fault detection using random projections and k-nearest neighbor rule for semiconductor manufacturing processes[J]. IEEE Transactions on Semiconductor Manufacturing, 2015, 28(1):70-79.
[17] HE Q P, WANG J. Principal component based k-nearest-neighbor rule for semiconductor process fault detection[C]//Proceedings of the 2008 American Control Conference. Piscataway, NJ:IEEE, 2008:1606-1611.
[18] VERDIER G, FERREIRA A. Fault detection with an adaptive distance for the k-nearest neighbors rule[C]//Proceedings of the 2009 International Conference on Computers & Industrial Engineering. Piscataway, NJ:IEEE, 2009:1273-1278.
[19] 冯立伟,张成,李元,等.基于局部马氏距离的加权k近邻故障检测方法[J].通化师范学院学报,2017,38(4):57-63.(FENG L W, ZHANG C, LI Y, et al. Local Mahalanobis distance based weighted k nearest neighbor rule for fault detection[J]. Journal of Tonghua Normal University, 2017, 38(4):57-63.)
[20] 李元,李美萱,张成,等.基于局部临近标准化的FD-kNN故障检测[J].山东科技大学学报(自然科学版),2017,36(5):1-6.(LI Y, LI M X, ZHANG C, et al. FD-kNN fault detection based on local nearest neighborhood standardization[J]. Journal of Shandong University of Science and Technology (Natural Science), 2017, 36(5):1-6).
[21] MA H, HU Y, SHI H. A novel local neighborhood standardization strategy and its application in fault detection of multimode processes[J]. Chemometrics & Intelligent Laboratory Systems, 2012, 118(7):287-300.
[22] MA H, HU Y, SHI H. Fault detection and identification based on the neighborhood standardized local outlier factor method[J]. Industrial & Engineering Chemistry Research, 2013, 52(6):2389-2402.
[23] 刘毅,王海清.Pensim仿真平台在青霉素发酵过程的应用研究[J].系统仿真学报,2006,18(12):3524-3527.(LIU Y, WANG H Q. Pensim simulator and its application in penicillin fermentation process[J]. Journal of System Simulation, 2006, 18(12):3524-3527.)
[24] NG Y S, SRINIVASAN R. An adjoined multi-model approach for monitoring batch and transient operations[J]. Computers & Chemical Engineering, 2009, 33(4):887-900.
[25] LEE J M, YOO C K, LEE I B. Fault detection of batch processes using multiway kernel principal component analysis[J]. Computers & Chemical Engineering, 2004, 28(9):1837-1847.

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

基于改进的局部近邻标准化和kNN的多阶段过程故障检测

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

[1]	YANG Xian, ZHAO Jisheng, QIANG Baohua, MI Luzhong, PENG Bo, TANG Chenghua, LI Baolian. Wind turbine fault sampling algorithm based on improved BSMOTE and sequential characteristics [J]. Journal of Computer Applications, 2021, 41(6): 1673-1678.
[2]	LU Rongxiu, CHEN Mingming, YANG Hui, ZHU Jianyong. Element component content dynamic monitoring system based on time sequence characteristics of solution images [J]. Journal of Computer Applications, 2021, 41(10): 3075-3081.
[3]	ZHANG Chenxi, TANG Shu, TANG Ke. Strategies of parameter fault detection for rocket engines based on transfer learning [J]. Journal of Computer Applications, 2020, 40(9): 2774-2780.
[4]	LI Dongbo, HUANG Lyuwen. Reweighted sparse principal component analysis algorithm and its application in face recognition [J]. Journal of Computer Applications, 2020, 40(3): 717-722.
[5]	HUANG Gong, ZHAO Yongping, XIE Yunlong. Fault detection for turboshaft engine based on local density weighted one-class SVM algorithm [J]. Journal of Computer Applications, 2020, 40(3): 917-924.
[6]	MAO Wentao, YANG Chao, LIU Yamin, TIAN Siyu. Deep domain adaptation model with multi-scale residual attention for incipient fault detection of bearings [J]. Journal of Computer Applications, 2020, 40(10): 2890-2898.
[7]	ZHANG Xiaobo, YANG Yan, LI Tianrui, LU Fan, PENG Lilan. Early diagnosis and prediction of Parkinson's disease based on clustering medical text data [J]. Journal of Computer Applications, 2020, 40(10): 3088-3094.
[8]	ZHOU Fei, XIA Pengcheng. Signal strength difference fingerprint localization algorithm based on principal component analysis and chi-square distance [J]. Journal of Computer Applications, 2019, 39(5): 1405-1410.
[9]	CHEN Wanzhi, XU Dongsheng, ZHANG Jing, TANG Yu. Intrusion detection method for industrial control system with optimized support vector machine and K-means++ [J]. Journal of Computer Applications, 2019, 39(4): 1089-1094.
[10]	WANG Shuo, WANG Peiliang. Fault detection method for batch process based on deep long short-term memory network and batch normalization [J]. Journal of Computer Applications, 2019, 39(2): 370-375.
[11]	WANG Xin, LI Ke, XU Mingjun, NING Chen. Improved remote sensing image classification algorithm based on deep learning [J]. Journal of Computer Applications, 2019, 39(2): 382-387.
[12]	WAN Yuan, ZHANG Jinghui, WU Kefeng, MENG Xiaojing. Image classification based on multi-layer non-negativity and locality Laplacian sparse coding [J]. Journal of Computer Applications, 2018, 38(9): 2489-2494.
[13]	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.
[14]	ZHANG Cheng, GUO Qingxiu, LI Yuan. Batch process monitoring based on k nearest neighbors in discriminated kernel principle component space [J]. Journal of Computer Applications, 2018, 38(8): 2185-2191.
[15]	FENG Liwei, ZHANG Cheng, LI Yuan, XIE Yanhong. Local outlier factor fault detection method based on statistical pattern and local nearest neighborhood standardization [J]. Journal of Computer Applications, 2018, 38(4): 965-970.