A local and global statistics pattern analysis method and its application to process fault identification

doi:10.1016/j.cjche.2015.09.004

Chinese Journal of Chemical Engineering ›› 2015, Vol. 23 ›› Issue (11): 1782-1792.DOI: 10.1016/j.cjche.2015.09.004

• 过程系统工程与过程安全 • 上一篇下一篇

A local and global statistics pattern analysis method and its application to process fault identification

Hanyuan Zhang, Xuemin Tian, Xiaogang Deng, Lianfang Cai

College of Information and Control Engineering, China University of Petroleum (East China), Qingdao 266580, China

收稿日期:2015-02-08 修回日期:2015-07-26 出版日期:2015-11-28 发布日期:2015-12-18
通讯作者: Xuemin Tian
基金资助:
Supported by the National Natural Science Foundation of China (61273160, 61403418), the Natural Science Foundation of Shandong Province (ZR2014FL016), and the Fundamental Research Funds for the Central Universities (14CX06132A).

A local and global statistics pattern analysis method and its application to process fault identification

Hanyuan Zhang, Xuemin Tian, Xiaogang Deng, Lianfang Cai

College of Information and Control Engineering, China University of Petroleum (East China), Qingdao 266580, China

Received:2015-02-08 Revised:2015-07-26 Online:2015-11-28 Published:2015-12-18
Contact: Xuemin Tian
Supported by:
Supported by the National Natural Science Foundation of China (61273160, 61403418), the Natural Science Foundation of Shandong Province (ZR2014FL016), and the Fundamental Research Funds for the Central Universities (14CX06132A).

摘要/Abstract

摘要： Traditional principal component analysis (PCA) is a second-ordermethod and lacks the ability to provide higherorder representations for data variables. Recently, a statistics pattern analysis (SPA) framework has been incorporated into PCAmodel tomake full use of various statistics of data variables effectively. However, thesemethods omit the local information, which is also important for process monitoring and fault diagnosis. In this paper, a local and global statistics pattern analysis (LGSPA) method, which integrates SPA framework and locality preserving projections within the PCA, is proposed to utilize various statistics and preserve both local and global information in the observed data. For the purpose of fault detection, two monitoring indices are constructed based on the LGSPA model. In order to identify fault variables, an improved reconstruction based contribution (IRBC) plot based on LGSPA model is proposed to locate fault variables. The RBC of various statistics of original process variables to the monitoring indices is calculated with the proposed RBC method. Based on the calculated RBC of process variables' statistics, a new contribution of process variables is built to locate fault variables. The simulation results on a simple six-variable system and a continuous stirred tank reactor system demonstrate that the proposed fault diagnosis method can effectively detect fault and distinguish the fault variables from normal variables.

关键词: Principal component analysis, Local structure analysis, Statistics pattern analysis, Reconstruction-based contribution, Fault diagnosis

Abstract: Traditional principal component analysis (PCA) is a second-ordermethod and lacks the ability to provide higherorder representations for data variables. Recently, a statistics pattern analysis (SPA) framework has been incorporated into PCAmodel tomake full use of various statistics of data variables effectively. However, thesemethods omit the local information, which is also important for process monitoring and fault diagnosis. In this paper, a local and global statistics pattern analysis (LGSPA) method, which integrates SPA framework and locality preserving projections within the PCA, is proposed to utilize various statistics and preserve both local and global information in the observed data. For the purpose of fault detection, two monitoring indices are constructed based on the LGSPA model. In order to identify fault variables, an improved reconstruction based contribution (IRBC) plot based on LGSPA model is proposed to locate fault variables. The RBC of various statistics of original process variables to the monitoring indices is calculated with the proposed RBC method. Based on the calculated RBC of process variables' statistics, a new contribution of process variables is built to locate fault variables. The simulation results on a simple six-variable system and a continuous stirred tank reactor system demonstrate that the proposed fault diagnosis method can effectively detect fault and distinguish the fault variables from normal variables.

Key words: Principal component analysis, Local structure analysis, Statistics pattern analysis, Reconstruction-based contribution, Fault diagnosis

Hanyuan Zhang, Xuemin Tian, Xiaogang Deng, Lianfang Cai. A local and global statistics pattern analysis method and its application to process fault identification[J]. Chinese Journal of Chemical Engineering, 2015, 23(11): 1782-1792.

Hanyuan Zhang, Xuemin Tian, Xiaogang Deng, Lianfang Cai. A local and global statistics pattern analysis method and its application to process fault identification[J]. Chin.J.Chem.Eng., 2015, 23(11): 1782-1792.

参考文献

[1] S.J. Qin, Survey on data-driven industrial process monitoring and diagnosis, Annu. Rev. Control. 36 (2) (2012) 220-234.

[2] J. Liang, J.X. Qian, Multivariate statistical processmonitoring and control: Recent developments and applications to chemical industry, Chin. J. Chem. Eng. 11 (2) (2003) 191-203.

[3] Z.F. Wang, J.Q. Yuan, Online supervision of penicillin cultivations based on rolling MPCA, Chin. J. Chem. Eng. 15 (1) (2007) 92-96.

[4] L.P. Zhao, C.H. Zhao, F.R. Gao, Phase transition analysis based quality prediction for multi-phase batch processes, Chin. J. Chem. Eng. 20 (6) (2012) 1191-1197.

[5] H. Guo, H.G. Li, On-line batch process monitoring with improved multi-way independent component analysis, Chin. J. Chem. Eng. 21 (3) (2013) 263-270.

[6] L.F. Cai, X.M. Tian, S. Chen, A process monitoring method based on noisy independent component analysis, Neurocomputing 127 (2014) 231-246.

[7] J. Wang, Q.P. He, Multivariate statistical process monitoring based on Statistics Pattern Analysis, Ind. Eng. Chem. Res. 49 (17) (2010) 7858-7869.

[8] Q.P. He, J. Wang, Statistics pattern analysis: A new process monitoring framework and its application to semiconductor batch processes, AICHE J. 57 (1) (2011) 107-121.

[9] H.J. Galicia, Q.P. He, J. Wang, Statistics pattern analysis based fault detection and diagnosis, Proceedings of CPC VIII Conference, 2012.

[10] H.Y. Zhang, X.M. Tian, X.G. Deng, Fault identificationmethod based on SPA similarity factor, CIESC J. 64 (12) (2013) 4503-4508 (in Chinese).

[11] X. Xie, H.B. Shi, Multimode process monitoring based on fuzzy C-means in locality preserving projection subspaces, Chin. J. Chem. Eng. 20 (6) (2012) 1174-1179.

[12] X.F. He, P. Niyogi, Locality preserving projections, NISP 16 (2003) 234-241.

[13] K.L. Hu, J.Q. Yuan, Multivariate statistical process control based on multiway locality preserving projections, J. Process Control 18 (7) (2008) 797-807.

[14] J.D. Shao, G. Rong, J.M. Lee, Generalized orthogonal locality preserving projections for nonlinear fault detection and diagnosis, Chemom. Intell. Lab. Syst. 96 (1) (2009) 75-83.

[15] X.G. Deng, X.M. Tian, Sparse kernel locality preserving projection and its application in nonlinear process fault detection, Chin. J. Chem. Eng. 21 (2) (2013) 163-170.

[16] M.G. Zhang, Z.Q. Ge, Z.H. Song, R.W. Fu, Global-local structure analysis model and its application for fault detection and identification, Ind. Eng. Chem. Res. 50 (11) (2011) 6387-6848.

[17] J.B. Yu, Local and global principal component analysis for process monitoring, J. Process Control 22 (7) (2012) 1358-1373.

[18] X.G. Deng, X.M. Tian, S. Chen, Modified kernel principal component analysis based on local structure analysis and its application to nonlinear process fault diagnosis, Chemom. Intell. Lab. Syst. 127 (2013) 195-209.

[19] J.A. Westerhuis, S.P. Gurden, A.K. Smilde, Generalized contribution plots inmultivariate statistical process monitoring, Chemom. Intell. Lab. Syst. 51 (1) (2000) 95-114.

[20] C.F. Alcala, S.J. Qin, Unified analysis of diagnosis methods for process monitoring, Proceedings of the 7th IFAC Symposium on Fault Detection Supervision and Safety of Technical Processes. Barcelona, Spain 2009, pp. 1-3.

[21] C.F. Alcala, S.J. Qin, Reconstruction-based contribution for process monitoring, Automatica 45 (7) (2009) 1593-1600.

[22] R. Dunia, S.J. Qin, Subspace approach tomultidimensional fault identification and reconstruction, AICHE J. 44 (8) (1998) 1813-1831.

[23] H.H. Yue, S.J. Qin, Reconstruction-based fault identification using a combined index, Ind. Eng. Chem. Res. 40 (20) (2001) 4403-4414.

[24] C.F. Alcala, S.J. Qin, Reconstruction-based contribution for process monitoring with kernel principal component analysis, Ind. Eng. Chem. Res. 49 (17) (2010) 7849-7857.

[25] X.G.Deng, X.M. Tian,A newfault isolationmethod based on unified contribution plots, Proceedings of the 30th Chinese Control Conference, Yantai 2011, pp. 4280-4285.

[26] P. Nomikos, J.F. Macgregor, Multivariate SPC charts for monitoring batch processes, Technometrics 37 (1) (1995) 41-59.

[27] S.J. Qin, Statistical process monitoring: Basics and beyond, J. Chemom. 17 (8) (2003) 480-502.

[28] E.B. Martin, A.J.Morris, Non-parametric confidence bounds for process performance monitoring charts, J. Process Control 6 (6) (1996) 349-358.

[29] Z.Q. Ge, L. Xie, U. Kruger, L. Lamont, Z.H. Song, S.Q.Wang, Sensor fault identification and isolation for multivariate non-Gaussian processes, J. Process Control 19 (10) (2009) 1707-1715.

[30] B. Daaou, A. Mansouri, M. Bouhamida, M. Chenafa, Development of linearizing feedback control with a variable structure observer for continuous stirred tank reactors, Chin. J. Chem. Eng. 20 (3) (2012) 567-571.

A local and global statistics pattern analysis method and its application to process fault identification

A local and global statistics pattern analysis method and its application to process fault identification

PDF (PC)

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	Dali Gao, Chunjie Yang, Bo Yang, Yu Chen, Ruilong Deng. Minimax entropy-based co-training for fault diagnosis of blast furnace[J]. 中国化学工程学报, 2023, 59(7): 231-239.
[2]	Xiongzhuo Zhu, Dali Gao, Chong Yang, Chunjie Yang. A blast furnace fault monitoring algorithm with low false alarm rate: Ensemble of greedy dynamic principal component analysis-Gaussian mixture model[J]. 中国化学工程学报, 2023, 57(5): 151-161.
[3]	Shanwei Xiong, Li Zhou, Yiyang Dai, Xu Ji. Attention-based long short-term memory fully convolutional network for chemical process fault diagnosis[J]. 中国化学工程学报, 2023, 56(4): 1-14.
[4]	Fangyuan Ma, Cheng Ji, Jingde Wang, Wei Sun. Early identification of process deviation based on convolutional neural network[J]. 中国化学工程学报, 2023, 56(4): 104-118.
[5]	Yiming Bai, Shuaiyu Xiang, Feifan Cheng, Jinsong Zhao. A dynamic-inner LSTM prediction method for key alarm variables forecasting in chemical process[J]. 中国化学工程学报, 2023, 55(3): 266-276.
[6]	Jiaxin Zhang, Wenjia Luo, Yiyang Dai, Yuman Yao. Cycle temporal algorithm-based multivariate statistical methods for fault diagnosis in chemical processes[J]. 中国化学工程学报, 2022, 47(7): 54-70.
[7]	Ruoshi Qin, Jinsong Zhao. Adaptive multiscale convolutional neural network model for chemical process fault diagnosis[J]. 中国化学工程学报, 2022, 50(10): 398-411.
[8]	Yuan Li, Dongsheng Yang. Local component based principal component analysis model for multimode process monitoring[J]. 中国化学工程学报, 2021, 34(6): 116-124.
[9]	Muhammad Nawaz, Abdulhalim Shah Maulud, Haslinda Zabiri, Syed Ali Ammar Taqvi, Alamin Idris. Improved process monitoring using the CUSUM and EWMA-based multiscale PCA fault detection framework[J]. 中国化学工程学报, 2021, 29(1): 253-265.
[10]	Chuankun Li, Dongfeng Zhao, Shanjun Mu, Weihua Zhang, Ning Shi, Lening Li. Fault diagnosis for distillation process based on CNN–DAE[J]. 中国化学工程学报, 2019, 27(3): 598-604.
[11]	Wende Tian, Yujia Ren, Yuxi Dong, Shaoguang Wang, Lingzhen Bu. Fault monitoring based on mutual information feature engineering modeling in chemical process[J]. 中国化学工程学报, 2019, 27(10): 2491-2497.
[12]	Zhengshun Fei, Kangling Liu. Online process monitoring for complex systems with dynamic weighted principal component analysis[J]. Chinese Journal of Chemical Engineering, 2016, 24(6): 775-786.
[13]	Yuan Xu, Ying Liu, Qunxiong Zhu. Multivariate time delay analysis based local KPCA fault prognosis approach for nonlinear processes[J]. , 2016, 24(10): 1413-1422.
[14]	Ying Tian, Wenli Du, Feng Qian. High dimension feature extraction based visualized SOM fault diagnosis method and its application in p-xylene oxidation process[J]. , 2015, 23(9): 1509-1517.
[15]	Kangling Liu, Xin Jin, Zhengshun Fei, Jun Liang. Adaptive partitioning PCA model for improving fault detection and isolation[J]. Chinese Journal of Chemical Engineering, 2015, 23(6): 981-991.