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化工学报  2015 

多SVDD模型的多模态过程监控方法

DOI: 10.11949/j.issn.0438-1157.20150479, PP. 4526-4533

Keywords: 多模态,复杂数据分布,局部离群概率,支持向量数据描述,过程监控

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Abstract:

现代工业过程往往具有多个运行模态,并且单一模态中的变量服从高斯与非高斯混合的复杂数据分布。针对多模态与复杂数据分布问题,基于局部离群概率(localoutlierprobability,LOOP)算法与支持向量数据描述(supportvectordatadescription,SVDD)算法,提出了一种名为MSVDD(multiplesupportvectordatadescription,MSVDD)的多模态过程监控方法。首先,考虑到不同模态之间存在差异,利用差分策略以及局部离群概率算法对多模态数据进行聚类。其次,在每个单一模态下分别建立SVDD模型。然后,通过计算测试样本对每个单一模态的离群概率选择合适的模型进行过程监控。最后,在TennesseeEastman(TE)平台上进行仿真测试以验证提出方法的可行性与有效性。

References

[1]  Kruger U, Dimitriadis G. Diagnosis of process faults in chemical systems using a local partial least squares approach [J]. AIChE J., 2008, 54: 2581-2596.
[2]  Lee J M, Yoo C K, Choi S W, Vanrolleghem P A, Lee I B. Nonlinear process monitoring using kernel principal component analysis [J]. Chem. Eng. Sci., 2004, 59: 223-234.
[3]  Ge Z Q, Song Z H. Mixture Bayesian regularization method of PPCA for multi-mode process monitoring [J]. AIChE J., 2010, 56: 2838-2849.
[4]  Jin H D, Lee Y H, Han C H. Robust recursive principle component analysis modeling for adapting monitoring [J]. Ind. Eng. Chem. Res., 2006, 45: 696-703.
[5]  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: 7025-7035.
[6]  Ma H H, Hu Y, Shi H B. Fault detection and identification based on the neighborhood standardized local outlier factor method [J]. Ind. Eng. Chem. Res., 2013, 52: 2389-2402.
[7]  Tan S, Wang F L, Peng J, Chang Y Q, Wang S. Multimode process monitoring based on mode identification [J]. Ind. Eng. Chem. Res., 2012, 51: 374-388.
[8]  Liu J, Chen D S. Fault detection and identification using modified Bayesian classification on PCA subspace [J]. Ind. Eng. Chem. Res., 2009, 48: 3059-3077.
[9]  Ge Z Q, Song Z H. Multimode process monitoring based on Bayesian method [J]. Journal of Chemometrics, 2009, 23: 636-650.
[10]  Xie X, Shi H B. Multimode process monitoring based on fuzzy C-means in locality preserving projection subspace [J]. Chinese J. Chem. Eng., 2012, 20:1174-1179.
[11]  Ge Z Q, Gao F R, Song Z H. Two-dimensional Bayesian monitoring method for nonlinear multimode processes [J]. Chem. Eng. Sci., 2011, 66: 5173-5183.
[12]  Choi S W, Park J H, Lee I B. Process monitoring using a Gaussian mixture model via principal component analysis and discriminant analysis [J]. Comput. Chem. Eng., 2004, 28: 1377-1387.
[13]  Yu J, Qin S J. Multimode process monitoring with Bayesian inference-based finite Gaussian mixture models [J]. AIChE J., 2008, 54:1811-1829.
[14]  Song B, Shi H B, Ma Y X, Wang J P. Multi-subspace principal component analysis with local outlier factor for multimode process monitoring [J]. Ind. Eng. Chem. Res., 2014, 53: 16453-16464.
[15]  Ge Z Q, Song Z H. Process monitoring based on independent component analysis-principal component analysis (ICA-PCA) and similarity factors [J]. Ind. Eng. Chem. Res., 2007, 46: 2054-2063.
[16]  Zhao C H, Gao F R, Wang F L. Nonlinear batch process monitoring using phase-based kernel-independent component analysis-principal component analysis (KICA-PCA) [J]. Ind. Eng. Chem. Res., 2009, 48: 9163-9174.
[17]  Ma Y X, Shi H B, Ma H H, Wang M L. Dynamic process monitoring using adaptive local outlier factor [J]. Chem. Intel. Lab. Syst., 2013, 127: 89-101.
[18]  Ge Z Q, Song Z H. Bagging support vector data description model for batch process monitoring [J]. J. Process Control, 2013, 23: 1090-1096.
[19]  Downs J J, Vogel E F. A plant-wide industrial process control problem [J]. Computers and Chemical Engineering, 1993, 17: 245-255.
[20]  Ricker N L. Optimal steady-state operation of the Tennessee Eastman challenge process [J]. Comput. Chem. Eng., 1995, 19: 949-959.
[21]  Wang L, Shi H B. Multivariate statistical process monitoring using an improved independent component analysis [J]. Chem. Eng. Res. Des., 2010, 88: 403-414.
[22]  Zhao C H, Gao F R. Fault-relevant principal component analysis (FPCA) method for multivariate statistical modeling and process monitoring [J]. Chemom. Intell. Lab. Syst., 2014, 133: 1-16.

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