Fault detection of complex chemical processes using Mahalanobis distance-based local outlier factor

doi:10.3969/j.issn.0438-1157.2013.05.024

CIESC Journal ›› 2013, Vol. 64 ›› Issue (5): 1674-1682.DOI: 10.3969/j.issn.0438-1157.2013.05.024

Previous Articles Next Articles

Fault detection of complex chemical processes using Mahalanobis distance-based local outlier factor

MA Hehe, HU Yi, SHI Hongbo

Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, Shanghai 200237, China

Received:2012-08-17 Revised:2012-12-12 Online:2013-05-05 Published:2013-05-05
Supported by:
supported by the National Natural Science Foundation of China(61074079)and Shanghai Leading Academic Discipline Project(B504).

基于马氏距离局部离群因子方法的复杂化工过程故障检测

马贺贺, 胡益, 侍洪波

华东理工大学化工过程先进控制和优化技术教育部重点实验室, 上海 200237

通讯作者: 侍洪波
作者简介:马贺贺(1985-),男,博士研究生。
基金资助:
国家自然科学基金项目(61074079);上海市重点学科项目(B504)。

Abstract

Abstract: Complex chemical processes often have multiple operating modes to meet the changes of production conditions.Meanwhile, process complexity makes the within-mode data usually follow an uncertain combination of Gaussian and non-Gaussian distributions.The multimode characteristics and the uncertainty of data distribution within one single mode make the conventional multivariate statistical process monitoring(MSPM)methods unsuitable for fault detection.In this paper, a novel Mahalanobis distance-based local outlier factor(MDLOF)method was proposed.The local structure of variables was taken into account by employing Mahalanobis distances, and the local density of the surrounding neighborhoods was also considered.A density-based monitoring statistics which was robust regardless of data distribution was obtained by this way.Finally, the validity and effectiveness of MDLOF were illustrated through a numerical example and the Tennessee Eastman process.

Key words: multimode process monitoring, fault detection, local outlier factor, Mahalanobis distance

摘要： 为了满足实际的生产需要,复杂化工过程往往包含多个运行模态。同时过程的复杂性使得同一模态下的数据分布是一种高斯分布和非高斯分布混合存在的不确定情况。数据的多模态分布特性以及同一模态下数据分布的不确定性使得传统多元统计监控(MSPM)方法很难给出令人满意的结果。针对这一问题,本文提出一种新的马氏距离局部离群因子(MDLOF)方法进行故障检测。通过利用马氏距离挖掘变量局部结构中包含的有用信息,并对样本的邻域密度加以考虑,形成对数据分布具有鲁棒性的基于密度的监控指标。最后通过数值仿真例子及Tennessee Eastman过程验证其有效性。

关键词: 多模态过程监控, 故障检测, 局部离群因子, 马氏距离

CLC Number:

TP277

MA Hehe, HU Yi, SHI Hongbo. Fault detection of complex chemical processes using Mahalanobis distance-based local outlier factor[J]. CIESC Journal, 2013, 64(5): 1674-1682.

马贺贺, 胡益, 侍洪波. 基于马氏距离局部离群因子方法的复杂化工过程故障检测[J]. 化工学报, 2013, 64(5): 1674-1682.

References 26

[1]	Qin S J.Statistical process monitoring:basics and beyond[J].J. Chemom.,2003, 17(8/9):480-502
[2]	Ge Z Q, Song Z H.Multimode process monitoring based on Bayesian method[J].J. Chemom.,2009, 23(12):636-650
[3]	Yu J, Qin S J.Multimode process monitoring with Bayesian inference-based finite Gaussian mixture models[J].AIChE J.,2008, 54(7):1811-1829
[4]	Wold S, Esbensen K, Geladi P.Principal component analysis[J].Chemom. Intell. Lab. Syst.,1987, 2(1/2/3):37-52
[5]	Nomikos P, Macgregor J F.Multi-way partial least squares in monitoring batch processes[J].Chemom. Intell. Lab. Syst.,1995, 30(1):97-108
[6]	Zhao S J, Zhang J, Xu Y M.Performance monitoring of processes with multiple operating modes through multiple PLS models[J].J. Process Control,2006, 16(7):763-772
[7]	Zhao S J, Zhang J, Xu Y M.Monitoring of processes with multiple operation modes through multiple principle component analysis models[J].Ind. Eng. Chem. Res.,2004, 43(22):7025-7035
[8]	Xie X, Shi H B.Dynamic multimode process modeling and monitoring using adaptive Gaussian mixture models[J].Ind. Eng. Chem. Res.,2012, 51(15):5497-5505
[9]	Yu J.A particle filter driven dynamic Gaussian mixture model approach for complex process monitoring and fault diagnosis[J].J. Process Control,2012, 22(4):778-788
[10]	Yu J.A nonlinear kernel Gaussian mixture model based inferential monitoring approach for fault detection and diagnosis of chemical processes[J].Chem. Eng. Sci.,2011, 68(1):506-519
[11]	Tan S, Wang F L, Peng J, et al.Multimode process monitoring based on mode identification[J].Ind. Eng. Chem. Res.,2012, 51(1):374-388
[12]	Wang F L, Tan S, Penga J, et al.Process monitoring based on mode identification for multi-mode process with transitions[J].Chemom. Intell. Lab. Syst.,2012, 110(1):144-155
[13]	Rashid M M, Yu J.Hidden Markov model based adaptive independent component analysis approach for complex chemical process monitoring and fault detection[J].Ind. Eng. Chem. Res.,2012, 51(15):5506-5514
[14]	Yu J B.Hidden Markov models combining local and global information for nonlinear and multimodal process monitoring[J].J. Process Control,2010, 20(3):344-359
[15]	Natarajan S, Srinivasan R.Multi-model based process condition monitoring of offshore oil and gas production process[J].Chem. Eng. Res. Des.,2010, 88(5/6):572-591
[16]	Ge Z Q, Gao F R, Song Z H.Two-dimensional Bayesian monitoring method for nonlinear multimode processes[J].Chem. Eng. Sci.,2011, 66(21):5173-5183
[17]	Breunig M M, Kriegel H, Ng R T, et al.LOF:identifying density-based local outliers//Proceeding of ACM SIGMOD International Conference on Management of Data.New York,2000
[18]	Chen S Y, Wang W, van Zuylen H.A comparison of outlier detection algorithms for ITS data[J].Expert Syst. Appl.,2010, 37(2):1169-1178
[19]	Duan L, Xu L, Guo F, et al.A local-density based spatial clustering algorithm with noise[J].Inform. Syst.,2007, 32(7):978-986
[20]	Ganeriwal S, Balzano L K, Srivastava M B.Reputation-based framework for high integrity sensor networks[J].ACM Trans. Sensor Networks,2008, 4(3):1-37
[21]	Gao J, Hu W, Zhang Z, et al.RKOF:Robust Kernel-based Local Outlier Detection//Advances in Knowledge Discovery and Data Mining[M]. Heidelberg:Springer, 2011:270-283
[22]	Ma H H, Hu Y, Shi H B.A novel local neighborhood standardization strategy and its application in fault detection of multimode processes[J].Chemom. Intell. Lab. Syst.,2012, 118:287-300
[23]	Silverman B W.Density Estimation for Statistics and Data Analysis[M].New York:Chapman and Hall, 1986
[24]	Chiang L H, Russell E, Braatz R D.Fault Detection and Diagnosis in Industrial Systems[M].London:Springer, 2001
[25]	Ricker N L.Optimal steady-state operation of the Tennessee Eastman challenge process[J].Comput. Chem. Eng.,1995, 19(9):949-959
[26]	Downs J J, Vogel E F.A plant-wide industrial process control problem[J].Comput. Chem. Eng.,1993, 17(3):245-255

Fault detection of complex chemical processes using Mahalanobis distance-based local outlier factor

基于马氏距离局部离群因子方法的复杂化工过程故障检测

PDF (PC)

Knowledge

Cited

Abstract

Cite this article

share this article

References 26

Related Articles 15

Recommended Articles

Metrics

Comments

[1]	Yihao ZHANG, Zhenlei WANG. Fault detection using grouped support vector data description based on maximum information coefficient [J]. CIESC Journal, 2023, 74(9): 3865-3878.
[2]	Yuanzhe SHAO, Zhonggai ZHAO, Fei LIU. Quality-related non-stationary process fault detection method by common trends model [J]. CIESC Journal, 2023, 74(6): 2522-2537.
[3]	Bing SONG, Chengfeng ZHENG, Hongbo SHI, Yang TAO, Shuai TAN. Research on quality-related fault detection method based on VAE-OCCA [J]. CIESC Journal, 2023, 74(4): 1630-1638.
[4]	Minghui YANG, Xiaoyue LIU, Xiaogang DENG, Mingyan LIAO, Chunwang HOU. Incipient fault detection for dynamic chemical processes based on weighted probability CVDA [J]. CIESC Journal, 2022, 73(9): 3963-3972.
[5]	Jiawang YONG, Qianqian ZHAO, Nenglian FENG. Fault diagnosis of proton exchange membrane fuel cell based on nonlinear dynamic model [J]. CIESC Journal, 2022, 73(9): 3983-3993.
[6]	Jinyu GUO, Zhe WANG, Yuan LI. Fault detection method based on kernel entropy independent component analysis [J]. CIESC Journal, 2022, 73(8): 3647-3658.
[7]	Kun WANG, Hongbo SHI, Shuai TAN, Bing SONG, Yang TAO. Local time difference constrained neighborhood preserving embedding algorithm for fault detection [J]. CIESC Journal, 2022, 73(7): 3109-3119.
[8]	Jinyu GUO, Wentao LI, Yuan LI. Application of adaptive algorithm of online reduced KECA in fault detection [J]. CIESC Journal, 2021, 72(8): 4227-4238.
[9]	LI Yuan, YANG Dongsheng, ZHAO Liying, ZHANG Cheng. Fault detection using hierarchical variational Gaussian mixture model and principal polynomial analysis [J]. CIESC Journal, 2021, 72(3): 1616-1626.
[10]	Xiaohui WANG, Yanjiang WANG, Xiaogang DENG, Zheng ZHANG. Industrial process fault detection using weighted deep support vector data description [J]. CIESC Journal, 2021, 72(11): 5707-5716.
[11]	Mingyue DENG, Jianchang LIU, Peng XU, Shubin TAN, Liangliang SHANG. New fault detection and diagnosis strategy for nonlinear industrial process based on KECA [J]. CIESC Journal, 2020, 71(5): 2151-2163.
[12]	Yu HAN, Junfang LI, Qiang GAO, Yu TIAN, Guogang YU. Fault detection based on fault discrimination enhanced kernel entropy component analysis algorithm [J]. CIESC Journal, 2020, 71(3): 1254-1263.
[13]	XU Jing,WANG Zhenlei,WANG Xin. Fault detection for chemical process based on nonlinear dynamic global-local preserving projections [J]. CIESC Journal, 2020, 71(12): 5655-5663.
[14]	Zhongjian SUN,Bo YANG,Chu QI,Hongguang LI. An extended logical analysis of data approach to fault detections of industrial hybrid systems [J]. CIESC Journal, 2020, 71(11): 5237-5245.
[15]	Lei YU, Xiaogang DENG, Yuping CAO, Kaiqi LU. Fault detection method of unequal-length batch process based on VGDTW-MCVA [J]. CIESC Journal, 2019, 70(9): 3441-3448.