基于核典型RF的过程故障诊断
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摘要
基于主元分析的旋转森林没有考虑特征的时序相关性,为此,提出一种基于核典型旋转森林的故障诊断方法,以提高非线性动态过程的故障诊断精度。所提方法通过未知非线性映射将随机森林特征投影到高维线性再生核Hilbert空间,利用典型变量分析提取变量的动态相关信息,得到不相关特征。采用核函数解决非线性映射未知的问题,为了避免传统核典型变量分析存在的核矩阵奇异问题,该研究在核主元空间提取典型变量,以训练决策树。该方法考虑了随机森林特征的非线性相关性和动态相关性,增加了决策树之间的差异性,有助于提高故障诊断精度。以田纳西-伊斯曼过程为例对所提方法的有效性进行了验证。
Traditional rotation forest based on principal component analysis does not consider time series correlation of features. Therefore, a fault diagnosis method based on kernel canonical rotation forest is proposed to improve fault diagnosis accuracy in nonlinear dynamic processes. For the proposed method,random forest features are projected to the high dimension linear reproducing kernel Hilbert space by using unknown nonlinear mapping. Canonical variate analysis is used to extract dynamic correlation information, and to produce irrelevant features. Kernel function is used to solve the unknown nonlinear mapping problem. In order to avoid kernel matrix singular problems in traditional kernel canonical variate analysis, canonical variables are extracted in kernel principal space, and used to train decision trees. The proposed method takes nonlinear correlation and dynamic correlation of random forest features into account. Meanwhile, the difference between decision trees is increased, which is helpful to improve the accuracy of fault diagnosis. The effectiveness of the proposed method is demonstrated through a case study of the Tennessee Eastman process.
Traditional rotation forest based on principal component analysis does not consider time series correlation of features. Therefore, a fault diagnosis method based on kernel canonical rotation forest is proposed to improve fault diagnosis accuracy in nonlinear dynamic processes. For the proposed method,random forest features are projected to the high dimension linear reproducing kernel Hilbert space by using unknown nonlinear mapping. Canonical variate analysis is used to extract dynamic correlation information, and to produce irrelevant features. Kernel function is used to solve the unknown nonlinear mapping problem. In order to avoid kernel matrix singular problems in traditional kernel canonical variate analysis, canonical variables are extracted in kernel principal space, and used to train decision trees. The proposed method takes nonlinear correlation and dynamic correlation of random forest features into account. Meanwhile, the difference between decision trees is increased, which is helpful to improve the accuracy of fault diagnosis. The effectiveness of the proposed method is demonstrated through a case study of the Tennessee Eastman process.
引文
[1] Chiang L H, Braatz R D, Russell E L. Fault Detection and Diagnosis in Industrial Systems[M]. Springer Science&Business Media, 2001.
[2]吴定会,翟艳杰,李意扬,等.基于辨识算法的风力机桨距执行器故障诊断[J].控制工程,2016,23(6):795-799.Wu D H, Zai Y J, Li Y Y, et al. Fault Diagnosis for Pitch Actuators of Wind Turbines Based on Identification[J] Control Engineering, 2016,23(6):795-799.
[3]杜振宁,向春枝.基于小波包分解和PCA的轴承故障诊断[J].控制工程,2016(6):812-815.Du Z N, Xiang C Z. A Wavelet Packet Decomposition and Principal Component Analysis Approach for Feature Extraction in Bearing Failure Vibration Signal[J]. Control Engineering, 2016(6):812-815.
[4] Venkatasubramanian V,Rengaswamy R, Kavuri S N, et al. A Review of Process Fault Detection and Diagnosis. III. Process History Based Methods[J]. Computers&Chemical Engineering, 2003, 27(3):327-346.
[5] Breiman L. Random Forests[J]. Machine learning, 2001,45(1):5-32.
[6] Breiman L, Friedman J, Stone C J, et al. Classification and Regression Trees[M]. CRC press, 1984.
[7] Rodriguez J J, Kuncheva L I, Alonso C J. Rotation Forest:A New Classifier Ensemble Method[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006, 28(10):1619-1630.
[8] Zhang L, Suganthan P N. Random Forests with Ensemble of Feature Spaces[J]. Pattern Recognition, 2014,47(10):3429-3437.
[9] D6sir C, Bernard S, Petitjean C, et al. One Class Random Forests[J].Pattern Recognition, 2013,46(12):3490-3506.
[10] Verikas A, Gelzinis A, Bacauskiene M. Mining Data with Random Forests:A Survey and Results Of New Tests[J]. Pattern Recognition,2011,44(2):330-349.
[11]庄进发,罗键,彭彦卿,等.基于改进随机森林的故障诊断方法研究[J].计算机集成制造系统,2009,15(4):777-785.Zhuang J F, Luo J, PENG Y Q, et al. Fault Diagnosis Method Based on Modified Random Forests[J]. Computer Integrated Manufacturing Systems, 2009,15(4):777-785.
[12] Ahmad I, Mabuchi H, Kano M, et al. Data-Based Fault Diagnosis of Power Cable System:Comparative Study of k-NN, ANN, Random Forest, and CART[C] The 18th IFAC World Congress, 2011:12880-12885.
[13] Yan W. Application of Random Forest to Aircraft Engine Fault Diagnosis[C] Conference on Computational Engineering in Systems Applications, 2006:468-475.
[14]胡青,孙才新,杜林,等.核主成分分析与随机森林相结合的变压器故障诊断方法[J].高电压技术,2010,07:1725-1729.Hu Q, Sun C X, Du L, et al. Transformer Fault Diagnosis Method Using Random Forests and Kernel Principle Component Analysis[J].Gaodianya Jishu/High Voltage Engineering,2010,36(7):1725-1729.
[15] Cerrada M, Zurita Q Cabrera D, et al. Fault Diagnosis in Spur Gears Based on Genetic Algorithm and Random Forest[J]. Mechanical Systems and Signal Processing, 2016,70:87-103.
[16] Lu X, Cao Y P, Tian X M, et al. A dynamic nonlinear process fault diagnosis method using Canonical rotation forest[C]. 35th Chinese Control Conference, 2016:6515-6520.
[17]方匡南,吴见彬,朱建平,等.随机森林方法研究综述[J].统计与信息论坛,2011,26(3):32-38.Fang K N, Wu J B, Zhu J P, et al. A Review of Technologies on Random Forests[J]. Statistics&Information Forum, 2011,26(3):32-38.
[18] Russell E L, Chiang L H, Braatz R D. Fault Detection in Industrial Processes Using Canonical Variate Analysis and Dynamic Principal Component Analysis[J]. Chemometrics and Intelligent Laboratory Systems, 2000,51(1):81-93.
[19]Samuel R T, Cao Y. Kernel Canonical Variate Analysis for Nonlinear Dynamic Process Monitoring[J]. IFAC-PapersOnLine, 2015, 48(8):605-610.
[20] Lee J M, Yoo C K, Choi S W, et al. Nonlinear Process Monitoring Using Kernel Principal Component Analysis[J]. Chemical Enginering Science, 2004,59(1):223-234.
[21] Lyman P R, Georgakis C. Plant-Wide Control of The Tennessee Eastman Problem[J]. Computers&Chemical Engineering, 1995,19(3):321-331.
[2]吴定会,翟艳杰,李意扬,等.基于辨识算法的风力机桨距执行器故障诊断[J].控制工程,2016,23(6):795-799.Wu D H, Zai Y J, Li Y Y, et al. Fault Diagnosis for Pitch Actuators of Wind Turbines Based on Identification[J] Control Engineering, 2016,23(6):795-799.
[3]杜振宁,向春枝.基于小波包分解和PCA的轴承故障诊断[J].控制工程,2016(6):812-815.Du Z N, Xiang C Z. A Wavelet Packet Decomposition and Principal Component Analysis Approach for Feature Extraction in Bearing Failure Vibration Signal[J]. Control Engineering, 2016(6):812-815.
[4] Venkatasubramanian V,Rengaswamy R, Kavuri S N, et al. A Review of Process Fault Detection and Diagnosis. III. Process History Based Methods[J]. Computers&Chemical Engineering, 2003, 27(3):327-346.
[5] Breiman L. Random Forests[J]. Machine learning, 2001,45(1):5-32.
[6] Breiman L, Friedman J, Stone C J, et al. Classification and Regression Trees[M]. CRC press, 1984.
[7] Rodriguez J J, Kuncheva L I, Alonso C J. Rotation Forest:A New Classifier Ensemble Method[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006, 28(10):1619-1630.
[8] Zhang L, Suganthan P N. Random Forests with Ensemble of Feature Spaces[J]. Pattern Recognition, 2014,47(10):3429-3437.
[9] D6sir C, Bernard S, Petitjean C, et al. One Class Random Forests[J].Pattern Recognition, 2013,46(12):3490-3506.
[10] Verikas A, Gelzinis A, Bacauskiene M. Mining Data with Random Forests:A Survey and Results Of New Tests[J]. Pattern Recognition,2011,44(2):330-349.
[11]庄进发,罗键,彭彦卿,等.基于改进随机森林的故障诊断方法研究[J].计算机集成制造系统,2009,15(4):777-785.Zhuang J F, Luo J, PENG Y Q, et al. Fault Diagnosis Method Based on Modified Random Forests[J]. Computer Integrated Manufacturing Systems, 2009,15(4):777-785.
[12] Ahmad I, Mabuchi H, Kano M, et al. Data-Based Fault Diagnosis of Power Cable System:Comparative Study of k-NN, ANN, Random Forest, and CART[C] The 18th IFAC World Congress, 2011:12880-12885.
[13] Yan W. Application of Random Forest to Aircraft Engine Fault Diagnosis[C] Conference on Computational Engineering in Systems Applications, 2006:468-475.
[14]胡青,孙才新,杜林,等.核主成分分析与随机森林相结合的变压器故障诊断方法[J].高电压技术,2010,07:1725-1729.Hu Q, Sun C X, Du L, et al. Transformer Fault Diagnosis Method Using Random Forests and Kernel Principle Component Analysis[J].Gaodianya Jishu/High Voltage Engineering,2010,36(7):1725-1729.
[15] Cerrada M, Zurita Q Cabrera D, et al. Fault Diagnosis in Spur Gears Based on Genetic Algorithm and Random Forest[J]. Mechanical Systems and Signal Processing, 2016,70:87-103.
[16] Lu X, Cao Y P, Tian X M, et al. A dynamic nonlinear process fault diagnosis method using Canonical rotation forest[C]. 35th Chinese Control Conference, 2016:6515-6520.
[17]方匡南,吴见彬,朱建平,等.随机森林方法研究综述[J].统计与信息论坛,2011,26(3):32-38.Fang K N, Wu J B, Zhu J P, et al. A Review of Technologies on Random Forests[J]. Statistics&Information Forum, 2011,26(3):32-38.
[18] Russell E L, Chiang L H, Braatz R D. Fault Detection in Industrial Processes Using Canonical Variate Analysis and Dynamic Principal Component Analysis[J]. Chemometrics and Intelligent Laboratory Systems, 2000,51(1):81-93.
[19]Samuel R T, Cao Y. Kernel Canonical Variate Analysis for Nonlinear Dynamic Process Monitoring[J]. IFAC-PapersOnLine, 2015, 48(8):605-610.
[20] Lee J M, Yoo C K, Choi S W, et al. Nonlinear Process Monitoring Using Kernel Principal Component Analysis[J]. Chemical Enginering Science, 2004,59(1):223-234.
[21] Lyman P R, Georgakis C. Plant-Wide Control of The Tennessee Eastman Problem[J]. Computers&Chemical Engineering, 1995,19(3):321-331.