基于半监督学习的故障诊断研究
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摘要
随着计算机技术和自动控制技术的发展,过程控制系统的复杂度和规模在不断提高,如果在系统运行过程中操作不当,或因一些突发情况,系统发生故障的可能性也在不断增大,这将导致无法估计的经济损失和人身安全隐患。同时随着自动控制理论、机器学习技术和数据挖掘技术的发展,故障诊断技术也得到了迅速的发展,如何利用故障诊断技术来有效提高系统安全性和可靠性,成为工业界和学术界研究的热点。
本文从半监督学习和流形学习相结合的角度出发,主要研究了基于半监督局部线性嵌入算法的故障诊断过程,本文主要工作和贡献如下:
(1)阐述了故障诊断的研究现状,并从基于模型和基于数据驱动两个方向对故障诊断技术的发展进行了详细的分析。其中重点研究了基于数据驱动的故障诊断技术,并根据数据本身的特性对该技术提出了一种新的分类方法,即分为监督学习的方法、非监督学习方法和半监督学习的方法。
(2)研究了半监督学习和流形学习理论,其中着重对半监督分类算法和流形学习中的各种非线性降维算法进行了研究,并与线性降维算法在Swiss roll数据上进行了实验对比。
(3)提出了一种基于半监督局部线性嵌入的故障诊断方法,该方法利用半监督局部线性嵌入算法对采集的数据进行特征提取后得到低维特征空间,在低维特征空间进行故障模式分类以达到故障诊断的目的。并利用半监督局部线性嵌入算法在故障数据上进行仿真,仿真结果表明该算法的故障分类效果明显,具备一定的实用价值。
(4)将半监督局部线性嵌入算法应用在TE模型和BSM1污水处理模型上,通过在模型上产生故障数据,验证半监督局部线性嵌入算法的有效性。实验结果表明,该算法能有效识别模型产生的故障,提升了故障诊断效果。
With the development of computer technology and automation technology, the complexity and the scale of the process control system are steadily rising, if the system is running in improper operation, or due to some unexpected circumstances, the possibility of system failure is also growing. This will result in incalculable economic loss and physical security vulnerabilities. At the same time, with the development of automatic control theory, machine learning techniques and data mining technology, fault diagnosis technology has got a rapid development. How to use it to effectively improve the security and reliability of the system, which has been the research hot for the industrial and academic fields.
From the viewpoint of the combination of semi-supervised learning and manifold learning, the main research of this paper is the fault diagnosis process based on semi-supervised locally linear embedding algorithm, and the work and contribution of this paper is as follows:
(1) describes the status quo of the fault diagnosis, and draws a detailed analysis from the both directions of model-based and data-driven. This paper mainly focus on fault-based diagnostic techniques based on data-driven, and presents a new classification method according to the characteristics of the data itself, which includes supervised learning methods, unsupervised learning methods and semi-supervised learning methods.
(2) studies the semi-supervised learning and manifold learning theory, which focuses on the semi-supervised classification algorithms and a variety of non-linear dimensionality reduction in algorithms manifold learning, and draws a comparison on Swiss roll data sets with the linear dimension reduction algorithms.
(3) proposes a fault diagnosis method based on semi-supervised locally linear embedding algorithm. We get low-dimensional feature space using semi-supervised locally linear embedding algorithm to the data collected for feature extraction. And we achieve the purpose of fault diagnosis in the low-dimensional feature space for fault pattern classification. We use this fault diagnosis method on fault simulation data sets, and simulation results show that the effect of fault classification algorithm is obvious, which have a certain practical value.
(4) The proposed method is used in TE model and BSM1 sewage treatment model. Using the model to generate fault data sets, we verify the proposed fault diagnosis method. Experimental results show that the method can effectively identify the faults generated by the models and to improve fault diagnosis performance.
本文从半监督学习和流形学习相结合的角度出发,主要研究了基于半监督局部线性嵌入算法的故障诊断过程,本文主要工作和贡献如下:
(1)阐述了故障诊断的研究现状,并从基于模型和基于数据驱动两个方向对故障诊断技术的发展进行了详细的分析。其中重点研究了基于数据驱动的故障诊断技术,并根据数据本身的特性对该技术提出了一种新的分类方法,即分为监督学习的方法、非监督学习方法和半监督学习的方法。
(2)研究了半监督学习和流形学习理论,其中着重对半监督分类算法和流形学习中的各种非线性降维算法进行了研究,并与线性降维算法在Swiss roll数据上进行了实验对比。
(3)提出了一种基于半监督局部线性嵌入的故障诊断方法,该方法利用半监督局部线性嵌入算法对采集的数据进行特征提取后得到低维特征空间,在低维特征空间进行故障模式分类以达到故障诊断的目的。并利用半监督局部线性嵌入算法在故障数据上进行仿真,仿真结果表明该算法的故障分类效果明显,具备一定的实用价值。
(4)将半监督局部线性嵌入算法应用在TE模型和BSM1污水处理模型上,通过在模型上产生故障数据,验证半监督局部线性嵌入算法的有效性。实验结果表明,该算法能有效识别模型产生的故障,提升了故障诊断效果。
With the development of computer technology and automation technology, the complexity and the scale of the process control system are steadily rising, if the system is running in improper operation, or due to some unexpected circumstances, the possibility of system failure is also growing. This will result in incalculable economic loss and physical security vulnerabilities. At the same time, with the development of automatic control theory, machine learning techniques and data mining technology, fault diagnosis technology has got a rapid development. How to use it to effectively improve the security and reliability of the system, which has been the research hot for the industrial and academic fields.
From the viewpoint of the combination of semi-supervised learning and manifold learning, the main research of this paper is the fault diagnosis process based on semi-supervised locally linear embedding algorithm, and the work and contribution of this paper is as follows:
(1) describes the status quo of the fault diagnosis, and draws a detailed analysis from the both directions of model-based and data-driven. This paper mainly focus on fault-based diagnostic techniques based on data-driven, and presents a new classification method according to the characteristics of the data itself, which includes supervised learning methods, unsupervised learning methods and semi-supervised learning methods.
(2) studies the semi-supervised learning and manifold learning theory, which focuses on the semi-supervised classification algorithms and a variety of non-linear dimensionality reduction in algorithms manifold learning, and draws a comparison on Swiss roll data sets with the linear dimension reduction algorithms.
(3) proposes a fault diagnosis method based on semi-supervised locally linear embedding algorithm. We get low-dimensional feature space using semi-supervised locally linear embedding algorithm to the data collected for feature extraction. And we achieve the purpose of fault diagnosis in the low-dimensional feature space for fault pattern classification. We use this fault diagnosis method on fault simulation data sets, and simulation results show that the effect of fault classification algorithm is obvious, which have a certain practical value.
(4) The proposed method is used in TE model and BSM1 sewage treatment model. Using the model to generate fault data sets, we verify the proposed fault diagnosis method. Experimental results show that the method can effectively identify the faults generated by the models and to improve fault diagnosis performance.
引文
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[3] WANG Hong, CHAI Tian-You, DING Jin-Liang, BROWN Martin. Data Driven Fault Diagnosis and Fault Tolerant Control: Some Advances and Possible New Directions. Acta Automatica Sinica, 2009, 35(6): 741-747
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[2]陈玉东,施颂椒,翁正新.动态系统的故障诊断方法综述.化工自动化及仪表. 2001, 28 (3) :1-14
[3] WANG Hong, CHAI Tian-You, DING Jin-Liang, BROWN Martin. Data Driven Fault Diagnosis and Fault Tolerant Control: Some Advances and Possible New Directions. Acta Automatica Sinica, 2009, 35(6): 741-747
[4] Isermann R. ,Balle P. Trends in the Application of Model Based Fault Detection and Diagnosis of Technical Processes . Proceedings of 13th IFAC World C ongress ,San Francisco ,USA ,1996 ,1212.
[5] Frank P. M. Fault Diagnosis in Dynamic Systems Using Analytical and Knowledge-based Redundancy—A Survey and S ome NewResults. Automatica ,1990 ,26 (3) :459-474.
[6]马笑潇.智能故障诊断中机器学习新理论机器应用[博士论文].重庆:重庆大学, 2002
[7] Qingsong Yang. MODEL-BASED AND DATA DRIVEN FAULT DIAGNOSIS METHODS WITH APPLICATIONS TO PROCESS MONITORING[博士论文]. CASE WESTERN RESERVE UNIVERSITY. 2004, 5
[8] Xiaojin Zhu. Semi-Supervised Learning Literature Survey. University of Wisconsin. 2008, 7
[9]蔡娟.控制系统的神经网络故障诊断方法[硕士论文].长沙:湖南大学.
[10] Timo Sorsa, Heikki N. Koivo, Senior Member, IEEE, Hannu Koivisto. Neural Networks in Process Fault Diagnosis. IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS. 1991, 21(4): 815-825
[11] Burak Ozyurt, Abraham Kandel*. A hybrid hierarchical neural network-fuzzy expert system approach to chemical process fault diagnosis. Fuzzy Sets and Systems. 1996, 83: 11-25
[12] Jian-DaWu , Yu-Hsuan Wang, Peng-Hsin Chiang , Mingsian R.Bai. A study of fault diagnosis in a scooter using adaptive order tracking technique and neural network. Expert Systems with Applications. 2009, 36: 49-56
[13] S.Simani, C.Fantuzzi. Fault diagnosis in power plant using neural networks. Information Sciences, 2000, 127: 125-136
[14] Chengcai Ma, Xiaodong Gu ,Yuanyuan Wang. Fault diagnosis of power electronic system based on fault gradation and neural network group. Neurocomputing, 2009, 72: 2909-2914
[15] Jie Zhang. Improved on-line process fault diagnosis through information fusion in multiple neural networks. Computers and Chemical Engineering. 2006, 30: 558-571
[16] Jie Zhang, Julian Morris. Process modelling and fault diagnosis using fuzzy neural networks[J]. Fuzzy Sets and Systems. 1996, 79: 127-140.
[17] Czeslaw T. Kowalski, Teresa Orlowska-Kowalska. Neural networks application for induction motor faults diagnosis. Mathematics and Computers in Simulation. 2003, 63, 435-448
[18]何学文.基于支持向量机的故障智能诊断理论与方法研究[硕士论文].长沙:中南大学.2004
[19] Sankar Mahadevan, Sirish L.Shah. Fault detection and diagnosis in process data using one-class support Vector machines. Journal of Process Control. 2009, 19: 1627-1639
[20] Xiuqiao Xiang, Jianzhong Zhou, Xueli An, Bing Peng, Junjie Yang. Fault diagnosis based on Walsh transform and support vector machine. Mechanical Systems and Signal Processing. 2008, 22: 1685-1693
[21] Ming Ge*, R.Du, Guicai Zhang,Yangsheng Xu. Fault diagnosis using support vector machine with an application in sheet metal stamping operations. Mechanical Systems and Signal Processing. 2004, 18: 143-159
[22] Hyun Joon Shina, Dong-Hwan Eomb, Sung-Shick Kim. One-class support vector machines an application In machine fault detection and classi?cation. Computers & Industrial Engineering. 2005, 48: 395-408
[23] CastroD, RansonA, MatheusJ, HemandezK. Fault detection and identification in chemical processes using multivariable statistical techniques and SVM for classification. Proceedings of ISA Monterrey 2002. North Carolina, 2002: 165-175
[24] Sheng-FaYuana, Fu-Lei Chu. Support vector machines based fault diagnosis for turbo-pump rotor. Mechanical Systems and Signal Processing. 2006, 20: 939-952
[25] Achmad Widodo, Bo-Suk Yang. Wavelet support vector machine for induction machine fault Diagnosis based on transient current signal. Expert Systems with Applications. 2008, 35: 307-316
[26]梁军.基于PCA模型的食品高温杀菌过程故障诊断技术研究.中国食品学报. 2003, 3(4): 10-16
[27] Wise B M, Ricker N L, Veltkamp D F, Kowalski B R. A Theoretical basis for the use of principal component models For monitoring multivariate processes. Process Control and Quality. 1990, 1(1): 41-51
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