模糊神经网络和D-S证据理论在齿轮箱故障诊断中的应用
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摘要
信息融合技术是将来自多传感器的信息和数据进行综合处理,从而做出正确、可靠的判断和决策,近年来在许多领域得到了广泛的应用和研究。在机械故障诊断中,可利用的信息很多,充分利用有用的信息对设备的故障进行诊断才能提高故障诊断的精度和准确性,因此信息融合技术是进行机械故障诊断的一种有效方法。
本文以齿轮箱为研究对象,分析了齿轮箱的故障机理,具体研究了齿轮箱中齿轮和轴承的故障,为齿轮箱故障诊断提供了理论依据。
针对模糊理论和神经网络在故障诊断中存在的不足和互补性,构建了一种结合两者优点的改进的模糊神经网络,并推导了相应的算法,建立了相应的故障诊断框架。利用改进的模糊神经网络对齿轮箱中的齿轮进行故障诊断,并与BP神经网络的诊断结果作对比,结果表明该方法的学习速度快、诊断精度高。
针对故障诊断中的不确定性,采用D-S证据理论进行故障诊断。在对D-S证据理论的基本概念和融合推理方法深入研究的基础上,建立了故障诊断框架,并提出应用改进的组合规则处理故障诊断中的冲突信息。算例证明经过融合可以有效的提高故障诊断的精度,而改进的D-S证据理论能有效的处理相冲突的信息。
为了提高诊断精度,本文设计了一种将模糊神经网络和D-S证据理论相结合的综合故障诊断方法,该方法是将模糊神经网络的初步诊断结果进行处理转化为基本概率赋值,然后利用D-S证据理论进行信息融合。对齿轮箱中齿轮和轴承故障诊断的结果验证了这种方法能够有效的提高故障诊断的精度。
Information fusion technology is used for deal with multi-sensor information by synthesis to make correct and reliable judgment, it has been researched and applied in many fields recently. There is much available information in mechanical fault diagnosis, only when the available information is used sufficiently, can the precision and incredibility be improved. So information fusion technology is a useful method to implement mechanical fault diagnosis.
The gear box is made as the research object, and the fault mechanism of gear box is analyzed in this dissertation. The fault of gear and bearing in gear box is studied in-depth, so the theory assists for the gear box fault diagnosis is provided.
For the shortage of fuzzy theory and neural network in fault diagnosis, a new fuzzy neural network structure which combines the merits of fuzzy theory and neural network is constructed and study arithmetic is showed in detail. Through the application in gearbox fault diagnosis and compared with the result from BP neural network, it has been proven that the fuzzy neural fault method is valid. It can be applied valuable in engineering field.
Aiming at the uncertainty information in fault diagnosis, this dissertation applies D-S evidence theory to fault diagnosis field. On the basis of research on the essential concept and fusion reasoning methods in D-S evidence theory, this dissertation establishes the framework of fault diagnosis, and uses the modified D-S evidence theory to deal with the conflicted information. The result of examples proves that this method can improve the precision of fault diagnosis effectively, and the modified D-S evidence theory can handle with conflicted information.
In order to improve the accuracy of fault diagnosis, an integrated fault diagnosis method that is based on fuzzy neural network and D-S evidence theory is presented in this dissertation. First it converts the preliminarily diagnosis result from fuzzy neural network to basic probability assignment, then use the D-S evidence theory to implement final fusion. The result of gear box fault diagnosis shows that this method can improve the accuracy of fault diagnosis effectively.
本文以齿轮箱为研究对象,分析了齿轮箱的故障机理,具体研究了齿轮箱中齿轮和轴承的故障,为齿轮箱故障诊断提供了理论依据。
针对模糊理论和神经网络在故障诊断中存在的不足和互补性,构建了一种结合两者优点的改进的模糊神经网络,并推导了相应的算法,建立了相应的故障诊断框架。利用改进的模糊神经网络对齿轮箱中的齿轮进行故障诊断,并与BP神经网络的诊断结果作对比,结果表明该方法的学习速度快、诊断精度高。
针对故障诊断中的不确定性,采用D-S证据理论进行故障诊断。在对D-S证据理论的基本概念和融合推理方法深入研究的基础上,建立了故障诊断框架,并提出应用改进的组合规则处理故障诊断中的冲突信息。算例证明经过融合可以有效的提高故障诊断的精度,而改进的D-S证据理论能有效的处理相冲突的信息。
为了提高诊断精度,本文设计了一种将模糊神经网络和D-S证据理论相结合的综合故障诊断方法,该方法是将模糊神经网络的初步诊断结果进行处理转化为基本概率赋值,然后利用D-S证据理论进行信息融合。对齿轮箱中齿轮和轴承故障诊断的结果验证了这种方法能够有效的提高故障诊断的精度。
Information fusion technology is used for deal with multi-sensor information by synthesis to make correct and reliable judgment, it has been researched and applied in many fields recently. There is much available information in mechanical fault diagnosis, only when the available information is used sufficiently, can the precision and incredibility be improved. So information fusion technology is a useful method to implement mechanical fault diagnosis.
The gear box is made as the research object, and the fault mechanism of gear box is analyzed in this dissertation. The fault of gear and bearing in gear box is studied in-depth, so the theory assists for the gear box fault diagnosis is provided.
For the shortage of fuzzy theory and neural network in fault diagnosis, a new fuzzy neural network structure which combines the merits of fuzzy theory and neural network is constructed and study arithmetic is showed in detail. Through the application in gearbox fault diagnosis and compared with the result from BP neural network, it has been proven that the fuzzy neural fault method is valid. It can be applied valuable in engineering field.
Aiming at the uncertainty information in fault diagnosis, this dissertation applies D-S evidence theory to fault diagnosis field. On the basis of research on the essential concept and fusion reasoning methods in D-S evidence theory, this dissertation establishes the framework of fault diagnosis, and uses the modified D-S evidence theory to deal with the conflicted information. The result of examples proves that this method can improve the precision of fault diagnosis effectively, and the modified D-S evidence theory can handle with conflicted information.
In order to improve the accuracy of fault diagnosis, an integrated fault diagnosis method that is based on fuzzy neural network and D-S evidence theory is presented in this dissertation. First it converts the preliminarily diagnosis result from fuzzy neural network to basic probability assignment, then use the D-S evidence theory to implement final fusion. The result of gear box fault diagnosis shows that this method can improve the accuracy of fault diagnosis effectively.
引文
1陈艳.基于信息融合的故障诊断方法.煤矿机械. 2006, 27(1):178～180
2韩晓明,杜长龙等.基于信息融合的机械故障诊断技术研究.煤炭科学技术. 2007, 35(3):86～89
3 L. Valet , G. Mauris. A Statistical Overview of Recent Literature in Information Fusion[J]. IEEE Tran Aerospace and Electronic Systems Magazine. 2001, 16(3):1～14
4 R. R. Tenney, N. R. Sandell. Detection with Distributed Sensors. IEEE Trans. on AES. 1981, 17(4):501～510
5 D. L. Hall, J. Lines. An Introduction to Multisensor Information Fusion. Proceedings of the IEEE. 1997, 85(1):6～23
6白云飞,曲尔光.多传感器信息融合技术及其应用.机械管理开发. 2008, 23(1):69～70
7吴伟等.多传感器融合实现机器人精确定位.东北大学学报(自然科学版). 2007, 28(2):161～164
8潘伟,王汉功.基于多传感器信息融合的工程机械液压系统在线状态监测与故障诊断.工程机械. 2004(7):42～46
9那彦,杨万海,李勇朝.图像信息融合与医学图像综合显示.西安电子科技大学学报(自然科学版). 2004, 31(1):21～24
10 N. Gang, H. Tian, et al. Multi-agent Decision Fusion for Motor Fault Diagnosis[J]. Mechanical Systems and Signal Processing. 2007, 21(3) :1285～1299
11夏虹,曹欣荣,王兆祥.基于传感器融合的机械设备故障诊断的方法与系统.哈尔滨工程大学学报. 1998, 19(4):52～57
12孟宪尧,白广来,伞宝钢等.贝叶斯数据融合技术在机舱故障诊断智能诊断中的应用.大连海事大学学报. 2002, 28(3):10～13
13何平,杨保华,王本利.模糊数据融合技术在系统故障诊断中的应用[J].电机与控制学报, 2004, 8(1):51～56
14 Yamada Kenichi, Ito Toshio. An Approach to Understanding Driving Environment using Network-type Sensor Fusion Method. Electronics and Communications in Japan, Part II: Electronics. 2004, 87(5):32～42
15 S. Zhang, J. Mathew, et al. Best basis-based Intelligent Machine Fault Diagnosis. Mechanical Systems and Signal Processing. 2005, 19(2):357～370
16 O. Basir, X. H. Yuan. Engine Fault Diagnosis based on Multi-sensor Information Fusion using Dempster-Shafer Evidence Theory[J]. Information Fusion. 2007, 8(4):379～386
17 S. Timo, N. K. Heikki. Neural Network in Process Fault Diagnosis. IEEE Trans on SMC. 1991,21(4):815～825
18 T. Yamamot, et al. Rough sets and partially-linearized neural network for structure fault diagnosis of rotating machinery. LNCS. 2004, (3174):555~580
19王建平,肖刚.齿轮传动故障诊断方法综述及应用研究.江苏船舶. 2008, 25(1):24~26
20于得介,程军胜. Hilbert能量谱及其在齿轮箱故障诊断中的应用.湖南大学学报. 2003, 30(4):47～50
21骆江锋,龙江启,范进桢.小波包和BP神经网络在齿轮箱故障诊断中的应用.机械传动. 2007, 31(3):84～88
22魏秀业,潘宏侠.齿轮箱故障诊断技术现状及展望.测试技术学报. 2006, 20(4):368～376
23眭小利.滚动轴承常见的失效形式分析和对策.铁道机车车辆工人. 2006, 12:15～18
24许昕,王晶禹,潘宏侠.利用神经网络对齿轮箱进行故障诊断的实例分析.机械工程与自动化. 2008, 1:99～101
25路人定.齿轮箱故障时域和频域综合诊断技术.机电工程技术. 2007, 36(3):17～19
26丁康,朱小勇,陈亚华.齿轮箱典型故障振动特征与诊断策略.振动与冲击. 2001, 20(3):7～13
27 Li Jihong, et al. Study on Fuzzy Fault Diagnosis for Complex System[J]. System Engineering and Electrics. 2005, 27(7):1322～1324
28 Jae Woo Lee, Sungzoon Cho. A Neural-network Method for Diagnosing Beam-position Monitors in Storage Ring. Nuclear Instruments and Methods in Physics Research. 1998, 402(1):14～20
29 Z. Jie. Improved on-line Process Fault Diagnosis Through Information Fusion in Multiple Neural Network[J]. Computers and Chemical Engineering,2006, 30(3):558～571
30 Meng Joo Er, Jun Liao. Fuzzy Neural Networks-based Quality Prediction System for Sintering Process. IEEE Transactions on Fuzzy Systems. 2000, 8(3):314～324
31 M. M. Gupta, D. H. Rao. On the Principles of Fuzzy Neural Networks. Fuzzy Sets System. 1994, 61(1):1～18
32 J. M. Keller, D. Hunt. Incorporating Fuzzy Membership Functions into the Perceptron Algorithm. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1985, 7(6):693～699
33 Special Issue on Fuzzy Logic and Neural Networks. IEEE Transactions on Neural Networks. 1992, 3(5):68～84
34 S. R. Jang. ANFIS: Adaptive-network-based Fuzzy Inference Systems. IEEE Trans. on Systems, Man and Cybernetics. 1993, 23(3):665～685
35 A. Nurnberger, D. Nauck, R. Kruse. Neuro-fuzzy Control based on the NEFCON-Model: Recent Developments. Soft Computing. 1999,2(4):168～182
36 T. Linh, S. Osowski. Neuro-fuzzy TSK Network for Approximation of Static and Dynamic Functions. Control and Cybernetics, 2002, 31(2):309～326
37魏海坤,徐嗣鑫,宋文忠.神经网络的泛化理论和泛化方法.自动化学报, 2001, 27(6):806~815
38张捷.基于神经网络的齿轮箱智能故障诊断技术的研究.江苏大学硕士论文. 2003:44～45
39 B. Samanta. Artificial Neural Network based Fault Diagnosis of Rolling Element Bearing using time Domain Features. Mechanical System and Signal Processing. 2003, 17(2):317~328
40 L. Steyer, L. Lardon, Q. Bernard. Sensors Network Diagnosis in Anaerobic Digestion Processes using Evidence Theory[J]. Water Science and Technology. 2004, 50(11):21～29
41马志刚,张文栋,王红亮. D-S改进算法在数据融合中的应用,《微计算机信息》(管控一体化). 2007,23(1):194～195
42许丽佳. D-S理论在信息融合中的改进[J].系统工程与电子技术. 2004, 26(6):717～720
43续媛君.基于Labview的齿轮箱故障诊断的研究与应用.中北大学硕士论文. 2007:44～48
44 Hermann Kaind. Object-oriented approach in software engineering and artificial intelligence. JOOP, 1994, 3:56~62
2韩晓明,杜长龙等.基于信息融合的机械故障诊断技术研究.煤炭科学技术. 2007, 35(3):86～89
3 L. Valet , G. Mauris. A Statistical Overview of Recent Literature in Information Fusion[J]. IEEE Tran Aerospace and Electronic Systems Magazine. 2001, 16(3):1～14
4 R. R. Tenney, N. R. Sandell. Detection with Distributed Sensors. IEEE Trans. on AES. 1981, 17(4):501～510
5 D. L. Hall, J. Lines. An Introduction to Multisensor Information Fusion. Proceedings of the IEEE. 1997, 85(1):6～23
6白云飞,曲尔光.多传感器信息融合技术及其应用.机械管理开发. 2008, 23(1):69～70
7吴伟等.多传感器融合实现机器人精确定位.东北大学学报(自然科学版). 2007, 28(2):161～164
8潘伟,王汉功.基于多传感器信息融合的工程机械液压系统在线状态监测与故障诊断.工程机械. 2004(7):42～46
9那彦,杨万海,李勇朝.图像信息融合与医学图像综合显示.西安电子科技大学学报(自然科学版). 2004, 31(1):21～24
10 N. Gang, H. Tian, et al. Multi-agent Decision Fusion for Motor Fault Diagnosis[J]. Mechanical Systems and Signal Processing. 2007, 21(3) :1285～1299
11夏虹,曹欣荣,王兆祥.基于传感器融合的机械设备故障诊断的方法与系统.哈尔滨工程大学学报. 1998, 19(4):52～57
12孟宪尧,白广来,伞宝钢等.贝叶斯数据融合技术在机舱故障诊断智能诊断中的应用.大连海事大学学报. 2002, 28(3):10～13
13何平,杨保华,王本利.模糊数据融合技术在系统故障诊断中的应用[J].电机与控制学报, 2004, 8(1):51～56
14 Yamada Kenichi, Ito Toshio. An Approach to Understanding Driving Environment using Network-type Sensor Fusion Method. Electronics and Communications in Japan, Part II: Electronics. 2004, 87(5):32～42
15 S. Zhang, J. Mathew, et al. Best basis-based Intelligent Machine Fault Diagnosis. Mechanical Systems and Signal Processing. 2005, 19(2):357～370
16 O. Basir, X. H. Yuan. Engine Fault Diagnosis based on Multi-sensor Information Fusion using Dempster-Shafer Evidence Theory[J]. Information Fusion. 2007, 8(4):379～386
17 S. Timo, N. K. Heikki. Neural Network in Process Fault Diagnosis. IEEE Trans on SMC. 1991,21(4):815～825
18 T. Yamamot, et al. Rough sets and partially-linearized neural network for structure fault diagnosis of rotating machinery. LNCS. 2004, (3174):555~580
19王建平,肖刚.齿轮传动故障诊断方法综述及应用研究.江苏船舶. 2008, 25(1):24~26
20于得介,程军胜. Hilbert能量谱及其在齿轮箱故障诊断中的应用.湖南大学学报. 2003, 30(4):47～50
21骆江锋,龙江启,范进桢.小波包和BP神经网络在齿轮箱故障诊断中的应用.机械传动. 2007, 31(3):84～88
22魏秀业,潘宏侠.齿轮箱故障诊断技术现状及展望.测试技术学报. 2006, 20(4):368～376
23眭小利.滚动轴承常见的失效形式分析和对策.铁道机车车辆工人. 2006, 12:15～18
24许昕,王晶禹,潘宏侠.利用神经网络对齿轮箱进行故障诊断的实例分析.机械工程与自动化. 2008, 1:99～101
25路人定.齿轮箱故障时域和频域综合诊断技术.机电工程技术. 2007, 36(3):17～19
26丁康,朱小勇,陈亚华.齿轮箱典型故障振动特征与诊断策略.振动与冲击. 2001, 20(3):7～13
27 Li Jihong, et al. Study on Fuzzy Fault Diagnosis for Complex System[J]. System Engineering and Electrics. 2005, 27(7):1322～1324
28 Jae Woo Lee, Sungzoon Cho. A Neural-network Method for Diagnosing Beam-position Monitors in Storage Ring. Nuclear Instruments and Methods in Physics Research. 1998, 402(1):14～20
29 Z. Jie. Improved on-line Process Fault Diagnosis Through Information Fusion in Multiple Neural Network[J]. Computers and Chemical Engineering,2006, 30(3):558～571
30 Meng Joo Er, Jun Liao. Fuzzy Neural Networks-based Quality Prediction System for Sintering Process. IEEE Transactions on Fuzzy Systems. 2000, 8(3):314～324
31 M. M. Gupta, D. H. Rao. On the Principles of Fuzzy Neural Networks. Fuzzy Sets System. 1994, 61(1):1～18
32 J. M. Keller, D. Hunt. Incorporating Fuzzy Membership Functions into the Perceptron Algorithm. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1985, 7(6):693～699
33 Special Issue on Fuzzy Logic and Neural Networks. IEEE Transactions on Neural Networks. 1992, 3(5):68～84
34 S. R. Jang. ANFIS: Adaptive-network-based Fuzzy Inference Systems. IEEE Trans. on Systems, Man and Cybernetics. 1993, 23(3):665～685
35 A. Nurnberger, D. Nauck, R. Kruse. Neuro-fuzzy Control based on the NEFCON-Model: Recent Developments. Soft Computing. 1999,2(4):168～182
36 T. Linh, S. Osowski. Neuro-fuzzy TSK Network for Approximation of Static and Dynamic Functions. Control and Cybernetics, 2002, 31(2):309～326
37魏海坤,徐嗣鑫,宋文忠.神经网络的泛化理论和泛化方法.自动化学报, 2001, 27(6):806~815
38张捷.基于神经网络的齿轮箱智能故障诊断技术的研究.江苏大学硕士论文. 2003:44～45
39 B. Samanta. Artificial Neural Network based Fault Diagnosis of Rolling Element Bearing using time Domain Features. Mechanical System and Signal Processing. 2003, 17(2):317~328
40 L. Steyer, L. Lardon, Q. Bernard. Sensors Network Diagnosis in Anaerobic Digestion Processes using Evidence Theory[J]. Water Science and Technology. 2004, 50(11):21～29
41马志刚,张文栋,王红亮. D-S改进算法在数据融合中的应用,《微计算机信息》(管控一体化). 2007,23(1):194～195
42许丽佳. D-S理论在信息融合中的改进[J].系统工程与电子技术. 2004, 26(6):717～720
43续媛君.基于Labview的齿轮箱故障诊断的研究与应用.中北大学硕士论文. 2007:44～48
44 Hermann Kaind. Object-oriented approach in software engineering and artificial intelligence. JOOP, 1994, 3:56~62