改进的粒子群优化算法的研究
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摘要
近年来,粒子群优化(Particle Swarm Optimization,PSO)算法这种模仿生物行为的智能优化算法,得到了较快的发展。因为具有较少的参数,并且概念容易理解,编码方便,所以人们将它广泛应用在工业上。同时,工业生产设备的关键零件轴承的状态,对设备的正常工作起到十分重要的作用,因此对轴承状态进行监测并对其故障进行诊断具有重要的现实意义。许多学者一直在研究将PSO优化算法与其他算法相结合,应用于故障诊断领域。本文从PSO与神经网络、PSO与聚类算法两个传统故障诊断方法的基础上,混合改进的粒子群优化算法,提出两种新型有效的轴承故障诊断方法。
首先,在分析MCPSO(Mulit-Species Cooperative PSO)算法原理以及优缺点的基础上,提出基于提高群体多样性的改进算法,并利用对不同的测试函数的仿真,确定发挥算法最优性能的参数。通过与其他几种优化算法的比较,证明新算法优异性能。
然后,分析PSO-K均值聚类算法的原理以及优缺点,并在此基础上提出改进的PSO-K均值聚类算法,利用来自UCI的数据对新算法进行性能评价试验,比较其同其他聚类算法在对低维以及高维数据分类中的不同性能。
最后,指出常见的轴承故障以及故障诊断方法,并用第一种算法结合神经网络,同BP算法比较以证明其良好的网络训练能力。将两种新算法应用在故障诊断领域,分别对轴承故障相关数据进行仿真试验,并通过跟其他几种方法的对比,表现出新算法在故障诊断率以及诊断效率上的优越性。
Particle Swarm Optimization algorithm developed rapidly in recent years, due to its simple concept, easy to implement, few parameters, etc. And as an important mechanical component, rotating machinery will directly affect the whole mechanical systems, so timely failure diagnosis is very important. So PSO has been successfully applied to the area of fault diagnosis. In this paper, two improved algorithms are proposed based on PSO-BP neural networks, PSO clustering algorithm.
Firstly, on the basic of Mulit-Species Cooperative PSO, the improved algorithm which increases the population diversity is proposed, through the simulation of the test functions, to determine the parameters which can make the algorithm performance best. Training the neural network instead of BP algorithm, then can prove the excellent performance of the new algorithm.
Then, the improved PSO-K means clustering algorithm is proposed, through the simulation of the datas from the UCI ,new algorithm performans well on the classification of low and high dimensional.
Finally, point out the the types of bearing fault and common methods of fault diagnosis, Training the neural network instead of BP algorithm, then can prove the excellent performance of the new algorithm.Make two new fault diagnosis algorithm applied in the field respectively,comparing with other methods, shows the advantages of the new algorithm on failure diagnosis and diagnostic efficiency.
首先,在分析MCPSO(Mulit-Species Cooperative PSO)算法原理以及优缺点的基础上,提出基于提高群体多样性的改进算法,并利用对不同的测试函数的仿真,确定发挥算法最优性能的参数。通过与其他几种优化算法的比较,证明新算法优异性能。
然后,分析PSO-K均值聚类算法的原理以及优缺点,并在此基础上提出改进的PSO-K均值聚类算法,利用来自UCI的数据对新算法进行性能评价试验,比较其同其他聚类算法在对低维以及高维数据分类中的不同性能。
最后,指出常见的轴承故障以及故障诊断方法,并用第一种算法结合神经网络,同BP算法比较以证明其良好的网络训练能力。将两种新算法应用在故障诊断领域,分别对轴承故障相关数据进行仿真试验,并通过跟其他几种方法的对比,表现出新算法在故障诊断率以及诊断效率上的优越性。
Particle Swarm Optimization algorithm developed rapidly in recent years, due to its simple concept, easy to implement, few parameters, etc. And as an important mechanical component, rotating machinery will directly affect the whole mechanical systems, so timely failure diagnosis is very important. So PSO has been successfully applied to the area of fault diagnosis. In this paper, two improved algorithms are proposed based on PSO-BP neural networks, PSO clustering algorithm.
Firstly, on the basic of Mulit-Species Cooperative PSO, the improved algorithm which increases the population diversity is proposed, through the simulation of the test functions, to determine the parameters which can make the algorithm performance best. Training the neural network instead of BP algorithm, then can prove the excellent performance of the new algorithm.
Then, the improved PSO-K means clustering algorithm is proposed, through the simulation of the datas from the UCI ,new algorithm performans well on the classification of low and high dimensional.
Finally, point out the the types of bearing fault and common methods of fault diagnosis, Training the neural network instead of BP algorithm, then can prove the excellent performance of the new algorithm.Make two new fault diagnosis algorithm applied in the field respectively,comparing with other methods, shows the advantages of the new algorithm on failure diagnosis and diagnostic efficiency.
引文
1 Kennedy J and Eberhart RC.Particle Swarm Optimization.Proceeding of IEEE International Conference on Neural Networks . Piscataway NJ : IEEE ,1995:1942~1948
2张燕,汪镭,康琦等.微粒群优化算法及其改进形式综述.计算机工程与应用.2005,41(2):1~3
3杨志鹏,朱丽莉,袁华.粒子群优化算法研究与发展.计算机工程与科学.2007,7(6):61~64
4杨维,李歧强.粒子群优化算法综述.中国工程科学.2004,5(5):87~94
5莫愿斌,刘贺同,陈德钊.粒子群优化算法的发展趋势.计算机与应用化学.2009,26(4) :1~2
6李宁,付国江,库少平等.粒子群优化算法的发展与展望.武汉理工大学学报.2005,27(2):26~29
7 Ozcan E,Mohan CK. Particle Swarm Optimzation: Surfing the Waves. In: Proc. of the IEEE Int’l Conf. on Evolutionary Computation. Washington: IEEE Inc, 1999. 1939~1944
8 Parsopoulos K,Vrahatis M.On The Computation of All Global Minimizers through Particle Swarm Optimization . IEEE Trans on Evolutionary Computation.2004, 8(3) :211~224
9鄂加强.智能故障诊断及其应用.长沙大学出版社,2006:56~58
10陶新民,杜宝祥,徐勇等.基于AR自相关峰态值的一类轴承故障检测方法.振动与冲击,2008,27(2):120~124
11 Shi Y H,Eberhart R.C.A Modified Particle Swarm Optimization,1998 IEEE. International Conference on Evolutionary Computation, Anchorage , Alaska, 1998:69~73
12 Mendes R,Kennedy J,The Full Informed Particle Swarm:Simpler, Maybe Better, IEEE transaction on Evolutionary Computation.2004,8(3) :204~210
13 Higashi N,Iba H.Particle Swarm Optimization with Gaussian Mutation. Institute of Electrical and Electronics Engineers.2003,(4) :72~79
14 Shi YH.Experimental Study of Particle Swarm Optimization.Proceedings of SCI Conference,Orlando,FL.2000: 123~126
15 Eberhart R C , Hu X , Human Tremor Analysis Using Particle SwarmOptimization . Proceedings of Congress on Evolutionary Computation ,Washington D C.1999:1927~1930
16杜玉平.关于粒子群算法改进的研究.西北大学硕士学位论文.2008: 96~101
17魏秀业.基于粒子群优化的齿轮箱智能故障诊断研究.中北大学博士学位论文.2009:115~118
18杜设亮,傅建中,陈子辰等.基于BP神经网络的齿轮箱故障诊断系统研究.机电工程.1999,16(5):81~82
19雷亚国,何正嘉等.混合聚类新算法及其在故障诊断中的应用.机械工程学报.2006,42(12):116~120
20欧阳晓黎,杨军.基于模式识别的装备故障诊断方法.弹舰与制导学报.2004, 24 (2) : 86~90
21 Yu liu,Zhen Qin,Zenglin Xu,and Xingshi He.Feature Selection with Particle Swarms.2004:425~430
22陈金山等.遗传+模糊C-均值混合聚类算法.电子与信息学报. 2002, 24(2): 210~215
23 Kennedy,J.,Eberhart,R.C. A Discrete Binary Version of the Particle Swarm Algorithm.On Systems,Man,and Cybernetics.Piscataway. IEEE Service Center.1997:4104~4109
24王凌,刘波.微粒群优化与调度算法.清华大学出版社,2008:95~101
25纪震,廖惠连,吴青华.粒子群算法及应用.科学出版社,2009:16~20
26 Shi Y H,Eberhart R.C.Fuzzy Adaptive Particle Swarm Optimizer.Proceedings of Congress On Evolutionary Computation, Korea : IEEE Service Center ,2001:654~660
27 Teleack.J.The Particle Swarm Optimization Algorithm:Convergence Analysis and Parameter Selection.Information Processing Letters.2003,85(9):317~325
28 LU Zhensu , HOU Zhirong . Particle Swarm Optimization with Adaptive Mutation.Acta Electronica Sinica.2004, 32(3): 416~420
29 Wang Junnian,Shen Q.Evolutionary Design of RBF Neural Network Based on Mulit-Species Cooperative Particle Swarm Optimizer . Control Theory&Applications,2006,32(2): 251~255
30赫然,王永吉.一种改进的自适应逃逸微粒群算法及实验分析.软件学报.2005,16(12) : 2036~2044
31 Wang Junnian, Shen Q.Evolutionary Design Of Feed-forward Neural Network Based On Sepecies Particle Swarm Optimizer,2005,20(9):981~985
32陶新民,徐晶,杨立标等.一种协调勘探和开采能力的粒子群算法.控制理论与应用,2010,27(5):636~640
33谢晓峰,杨文俊.微粒群算法综述.控制与决策.2003,18(2):129 ~ 134
34 Ioan Cristian.The Particle Swarm Optimization Algorithm:Convergence Analysis And Parameter Selection.Information Processing Letters 2003,85:317~325
35 Clerc M,Kennedy J.The Particle Swarm Explosion, Stability, And Convergence In A Multidimensional Complex Space . IEEE Trans . on Evolutionary Computation.2002, 6 (2) : 58~73
36张丽平.粒子群优化算法的理论及实践.浙江大学博士学位论文.2005:20~25
37 Boualem Boashash. Estimating and Interpreting the Instantaneous Frequency of a Signal- Part 1: Fundamentals. Proceedings of the IEEE. 1992, 80(4): 520~538
38 Krink T, Vesterstrom JS, Riget J.Particle Swarm Optimization with Spatial Particle Extension.In: Proc. of the IEEE Int’l Conf. on Evolutionary Computation. Honolulu: IEEE Inc.2002:1474~1497
39 Bocaniala CD,da Costa JS,Palade V.Refinement of The Diagnosis Process Performed with a Fuzzy Classifier.Lecture Notes in Artificial Intelligence,2004,3215:365~372
40刘靖明,韩丽川.基于粒子群的K均值聚类算法.系统工程理论与实践.2005,6:54~58
41陶新民,徐晶,杨立标等.一种改进的粒子群+K均值混合聚类算法.电子与信息学报,2010,32(1):92~97
42 Maulik U , Bandyopadhay S . Genetic Algorithm-Based Clustering Technique.Pattern Recognition.2000, 33(9):1455~1465
43 Central Research Establishment Home Office Forensic Science Service Aldermaston, Reading, Berkshire.1998:45~46
44李祥飞,邹恩,张泰山.基于混沌变量的前向神经网络组成优化设计.控制与决策.2003, 18 (6):705~707
45 Chen Guo, Feature Extraction and Intell igent Diagnosis for Ball Bearing Early Faults.Acta Aeronautica ET Astronautica Sinica.2009,30(2):362~367
46王涛,王晓霞.基于改进PSO-BP算法的变压器故障诊断.中国电力.2009,42(5), 13~16
47潘宏侠,魏秀业,马清峰.粒子群优化算法在传动箱故障诊断中的应用.火炮发射与控制学报.2006:44~46
48段晓东,王存睿,刘向东.粒子群算法及其应用.辽宁大学出版社,2007:136~140
2张燕,汪镭,康琦等.微粒群优化算法及其改进形式综述.计算机工程与应用.2005,41(2):1~3
3杨志鹏,朱丽莉,袁华.粒子群优化算法研究与发展.计算机工程与科学.2007,7(6):61~64
4杨维,李歧强.粒子群优化算法综述.中国工程科学.2004,5(5):87~94
5莫愿斌,刘贺同,陈德钊.粒子群优化算法的发展趋势.计算机与应用化学.2009,26(4) :1~2
6李宁,付国江,库少平等.粒子群优化算法的发展与展望.武汉理工大学学报.2005,27(2):26~29
7 Ozcan E,Mohan CK. Particle Swarm Optimzation: Surfing the Waves. In: Proc. of the IEEE Int’l Conf. on Evolutionary Computation. Washington: IEEE Inc, 1999. 1939~1944
8 Parsopoulos K,Vrahatis M.On The Computation of All Global Minimizers through Particle Swarm Optimization . IEEE Trans on Evolutionary Computation.2004, 8(3) :211~224
9鄂加强.智能故障诊断及其应用.长沙大学出版社,2006:56~58
10陶新民,杜宝祥,徐勇等.基于AR自相关峰态值的一类轴承故障检测方法.振动与冲击,2008,27(2):120~124
11 Shi Y H,Eberhart R.C.A Modified Particle Swarm Optimization,1998 IEEE. International Conference on Evolutionary Computation, Anchorage , Alaska, 1998:69~73
12 Mendes R,Kennedy J,The Full Informed Particle Swarm:Simpler, Maybe Better, IEEE transaction on Evolutionary Computation.2004,8(3) :204~210
13 Higashi N,Iba H.Particle Swarm Optimization with Gaussian Mutation. Institute of Electrical and Electronics Engineers.2003,(4) :72~79
14 Shi YH.Experimental Study of Particle Swarm Optimization.Proceedings of SCI Conference,Orlando,FL.2000: 123~126
15 Eberhart R C , Hu X , Human Tremor Analysis Using Particle SwarmOptimization . Proceedings of Congress on Evolutionary Computation ,Washington D C.1999:1927~1930
16杜玉平.关于粒子群算法改进的研究.西北大学硕士学位论文.2008: 96~101
17魏秀业.基于粒子群优化的齿轮箱智能故障诊断研究.中北大学博士学位论文.2009:115~118
18杜设亮,傅建中,陈子辰等.基于BP神经网络的齿轮箱故障诊断系统研究.机电工程.1999,16(5):81~82
19雷亚国,何正嘉等.混合聚类新算法及其在故障诊断中的应用.机械工程学报.2006,42(12):116~120
20欧阳晓黎,杨军.基于模式识别的装备故障诊断方法.弹舰与制导学报.2004, 24 (2) : 86~90
21 Yu liu,Zhen Qin,Zenglin Xu,and Xingshi He.Feature Selection with Particle Swarms.2004:425~430
22陈金山等.遗传+模糊C-均值混合聚类算法.电子与信息学报. 2002, 24(2): 210~215
23 Kennedy,J.,Eberhart,R.C. A Discrete Binary Version of the Particle Swarm Algorithm.On Systems,Man,and Cybernetics.Piscataway. IEEE Service Center.1997:4104~4109
24王凌,刘波.微粒群优化与调度算法.清华大学出版社,2008:95~101
25纪震,廖惠连,吴青华.粒子群算法及应用.科学出版社,2009:16~20
26 Shi Y H,Eberhart R.C.Fuzzy Adaptive Particle Swarm Optimizer.Proceedings of Congress On Evolutionary Computation, Korea : IEEE Service Center ,2001:654~660
27 Teleack.J.The Particle Swarm Optimization Algorithm:Convergence Analysis and Parameter Selection.Information Processing Letters.2003,85(9):317~325
28 LU Zhensu , HOU Zhirong . Particle Swarm Optimization with Adaptive Mutation.Acta Electronica Sinica.2004, 32(3): 416~420
29 Wang Junnian,Shen Q.Evolutionary Design of RBF Neural Network Based on Mulit-Species Cooperative Particle Swarm Optimizer . Control Theory&Applications,2006,32(2): 251~255
30赫然,王永吉.一种改进的自适应逃逸微粒群算法及实验分析.软件学报.2005,16(12) : 2036~2044
31 Wang Junnian, Shen Q.Evolutionary Design Of Feed-forward Neural Network Based On Sepecies Particle Swarm Optimizer,2005,20(9):981~985
32陶新民,徐晶,杨立标等.一种协调勘探和开采能力的粒子群算法.控制理论与应用,2010,27(5):636~640
33谢晓峰,杨文俊.微粒群算法综述.控制与决策.2003,18(2):129 ~ 134
34 Ioan Cristian.The Particle Swarm Optimization Algorithm:Convergence Analysis And Parameter Selection.Information Processing Letters 2003,85:317~325
35 Clerc M,Kennedy J.The Particle Swarm Explosion, Stability, And Convergence In A Multidimensional Complex Space . IEEE Trans . on Evolutionary Computation.2002, 6 (2) : 58~73
36张丽平.粒子群优化算法的理论及实践.浙江大学博士学位论文.2005:20~25
37 Boualem Boashash. Estimating and Interpreting the Instantaneous Frequency of a Signal- Part 1: Fundamentals. Proceedings of the IEEE. 1992, 80(4): 520~538
38 Krink T, Vesterstrom JS, Riget J.Particle Swarm Optimization with Spatial Particle Extension.In: Proc. of the IEEE Int’l Conf. on Evolutionary Computation. Honolulu: IEEE Inc.2002:1474~1497
39 Bocaniala CD,da Costa JS,Palade V.Refinement of The Diagnosis Process Performed with a Fuzzy Classifier.Lecture Notes in Artificial Intelligence,2004,3215:365~372
40刘靖明,韩丽川.基于粒子群的K均值聚类算法.系统工程理论与实践.2005,6:54~58
41陶新民,徐晶,杨立标等.一种改进的粒子群+K均值混合聚类算法.电子与信息学报,2010,32(1):92~97
42 Maulik U , Bandyopadhay S . Genetic Algorithm-Based Clustering Technique.Pattern Recognition.2000, 33(9):1455~1465
43 Central Research Establishment Home Office Forensic Science Service Aldermaston, Reading, Berkshire.1998:45~46
44李祥飞,邹恩,张泰山.基于混沌变量的前向神经网络组成优化设计.控制与决策.2003, 18 (6):705~707
45 Chen Guo, Feature Extraction and Intell igent Diagnosis for Ball Bearing Early Faults.Acta Aeronautica ET Astronautica Sinica.2009,30(2):362~367
46王涛,王晓霞.基于改进PSO-BP算法的变压器故障诊断.中国电力.2009,42(5), 13~16
47潘宏侠,魏秀业,马清峰.粒子群优化算法在传动箱故障诊断中的应用.火炮发射与控制学报.2006:44~46
48段晓东,王存睿,刘向东.粒子群算法及其应用.辽宁大学出版社,2007:136~140
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