车用燃料电池系统故障诊断与维护若干关键问题研究
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摘要
作为燃料电池汽车的核心单元,车用燃料电池系统由于结构复杂、运行环境恶劣以及动态响应能力较差等原因在实际应用中难免出现失效、故障甚至重大事故,因此,对其开展故障诊断研究对提高其自身以及燃料电池汽车的安全性、可靠性、可维护性以及市场接受能力具有重要意义。本文以自主研发的60kW车用燃料电池系统为对象,对整体系统安全性与可维护性、辅助系统和电堆的可靠性等涉及到的若干关键问题进行了研究。本文的主要研究成果如下：
较系统地提出了车用燃料电池系统故障特征与故障机理的共性问题,分别从耦合性、渐变性、随机性和不确定方面定性分析了其故障特征,从机械因素、电气因素、设备因素、人为因素和环境因素方面定性分析了其故障机理,对自主研发的车用燃料电池系统进行了故障分类并划分了其故障等级。
针对车用燃料电池系统的氢安全,建立了氢气泄漏的故障树模型并进行了定性分析,找出了系统氢安全的薄弱环节；为了进行系统氢气泄漏的安全性定量评价,考虑到各底事件的发生概率难以精确化,将模糊数学理论与传统故障树相结合定量分析了顶事件发生的模糊概率以及各个底事件的模糊重要度,并提出了系统氢气泄漏的故障诊断策略与整改措施。
为了进行辅助系统的多传感器故障检测与信号容错,提出并建立了一种主、从两级神经网络的联合故障诊断与容错模型,利用主网络和各个子网络的预测值与多传感器的实际输出值构建残差,当主网络的误差大于所设定的阈值并且持续的时间节拍大于一定值时判断传感器存在故障,此时利用误差超过设定阈值的子网络的预测值代替所对应的故障传感器输出值并进行在线刷新,针对不同数量下的传感器同时发生故障情况进行了仿真,仿真结果表明,所建立的模型可以实现这些存在冗余关系的多传感器的故障联合检测和信号冗余重构,能满足车用燃料电池系统的温度和风量容错控制要求。
考虑到电堆水淹和膜干两种故障难以准确识别,提出了二叉树支持向量机和D-S证据理论相结合的电堆健康诊断信息融合方法,从影响和反映电堆水平衡传输的实时参数及其变化参数中提取故障特征,将二叉树支持向量机进行初步诊断,利用D-S证据理论进行融合得出最终诊断结果。仿真结果表明,单独利用支持向量机比神经网络具有更高的诊断精度和识别速度,对于少数测试样本进行诊断时证据冲突所带来的不确定性,利用D-S证据理论进行融合后得出的电堆健康状态诊断结论具有较高的信任度,不确定性明显降低,可为电堆的湿度软测量与优化控制提供便利。
最后,结合车用燃料电池系统离线和在线故障诊断与实际维护的需要,开发了一种嵌入式的手持故障诊断仪,设计了实用的故障码并提出了故障诊断策略与流程,此外,基于VC++编程语言设计了可视化的状态监测与故障诊断专家系统,完成了原型机基本功能的开发,诊断实例和结果证明了其实用性。
综上所述,本文分别从氢气泄漏、冗余多传感器的故障诊断与容错、电堆健康状态诊断以及状态监测与故障诊断系统设计这4个关键问题出发,提出了它们的研究方法和思路,相关计算和仿真结果验证了它们的可行性和有效性,可为今后进一步开展车用燃料电池系统故障诊断与维护所涉及到的其它问题研究提供一些参考。
As the dominating part of Fuel Cell Electric Vehicle(FCEV), atuomotive fuel cell system can hardly avoid malfunction, faults or even serious accidents for its complicated structure, atrocious operational circumstance and poor dynamic permformance, so it is important to study on its fault diagonosis to enhance the safety, relability, maintainability and market acceptance of both itself and FCEV. Based on the 60kW automotive fuel cell system designed by our group, in this paper, some key problems related to the safety and maintainability of the whole system, and the relability of its accessorial subsystems and fuel cell stack are studied. The main research works are as follows:
The common problems of automotive fuel cell system's fault characteristic and fault mechanism are put forward systemically, the fault characteristic is qualitatively analyzed from the points of coupling, gradual change, randomicity and uncertainty, and the fault mechanism is qualitatively analyzed from the aspects of mechanism factor, electric fator, device factors, human factors and environmental factors, based on the automotive fuel cell system designed, its faults are classified and ranked.
According to the hydrogen safety of the whole system, the fault tree analysis model of hydrogen leaking is established and qualitatively analyzed, its weakness is figured out, to evaluate the hydrogen safety, considering the uncertain fault probability of each elementary event, the fuzzy maths theory is combined into the traditional fault tree analysis method to quantitatively analyze the probability of top event and the importance degree of each elementary event, then some improvement measures and advice on hydrogen leaking are brought forward accordingly.
As to the fault dianosis and signals fault tolerant of multisensors in the accessorial systems, the united fault diagnosis and fault tolerant model based on two layer main and sub neural networks is set up, the difference between the predictive output of the main neural network and subnetworks with the sensors's practical output is constructed, these sensors are diagnosed to be in fault when the difference of the main network exceeds the threshold setted and lasts for the threshold steps time, and the output of certain sub network is used to replace the practical output of its corresponding sensor. Finally, the simulation is presented, it proves that the model set up can be effectively used to the fault diagosis and signals restoration of those sensors which have redundancey relationship, and it can meet the needs of the both temperature and air flow fault tolerant control of automotive fuel cell system.
Considering flooding and drying of fuel cell stack are difficult to be inerrably distinguished, an information fusion fault dignosis method combining binary tree support vector machine(SVM) and D-S evident theory is put forward, the fault characteristic is extracted from the real time process variables and their variational parameters feature which affect and reflect the water transmission in fuel cell stack, the output of binary tree SVM is utilized as the initial healthy state diagosis results, then they are fused with D-S evident theory to get the final fault dignosis conclusion. The simulation results reveal that, compared with neural networks it has preponderant precision and speed with binary tree SVM, and the belief degree is high and the uncertainty of the conclusion is largely lowered down when the conflicting results of SVMs are fused with D-S evident theory.
Finally, according to the requirement of off-line and online fault diagosis and maintenance, an embedded handheld fault diagosis instrument is designed, the diagnostic trouble code(DTC) is designed and the fualt diagnosis strategy and flow are provided. Besides, the visual state monitoring and fault diagnosis expert ssystem is designed with VC++ languange, the specific interfaces are realized, and the fault diagnosis example and its results prove their practicability.
Above all, hydrogen leaking, multi sensor fault diagnosis and tolerant, fuel cell stack healthy state diagnosis, the design of monitoring and fault diagnosis system are studied in this paper, the calculation and simulation results show the methods and ideas adopted are effective and practical, which can provide some reference to some other problems on its fault dignosis and maintenance in future.
较系统地提出了车用燃料电池系统故障特征与故障机理的共性问题,分别从耦合性、渐变性、随机性和不确定方面定性分析了其故障特征,从机械因素、电气因素、设备因素、人为因素和环境因素方面定性分析了其故障机理,对自主研发的车用燃料电池系统进行了故障分类并划分了其故障等级。
针对车用燃料电池系统的氢安全,建立了氢气泄漏的故障树模型并进行了定性分析,找出了系统氢安全的薄弱环节；为了进行系统氢气泄漏的安全性定量评价,考虑到各底事件的发生概率难以精确化,将模糊数学理论与传统故障树相结合定量分析了顶事件发生的模糊概率以及各个底事件的模糊重要度,并提出了系统氢气泄漏的故障诊断策略与整改措施。
为了进行辅助系统的多传感器故障检测与信号容错,提出并建立了一种主、从两级神经网络的联合故障诊断与容错模型,利用主网络和各个子网络的预测值与多传感器的实际输出值构建残差,当主网络的误差大于所设定的阈值并且持续的时间节拍大于一定值时判断传感器存在故障,此时利用误差超过设定阈值的子网络的预测值代替所对应的故障传感器输出值并进行在线刷新,针对不同数量下的传感器同时发生故障情况进行了仿真,仿真结果表明,所建立的模型可以实现这些存在冗余关系的多传感器的故障联合检测和信号冗余重构,能满足车用燃料电池系统的温度和风量容错控制要求。
考虑到电堆水淹和膜干两种故障难以准确识别,提出了二叉树支持向量机和D-S证据理论相结合的电堆健康诊断信息融合方法,从影响和反映电堆水平衡传输的实时参数及其变化参数中提取故障特征,将二叉树支持向量机进行初步诊断,利用D-S证据理论进行融合得出最终诊断结果。仿真结果表明,单独利用支持向量机比神经网络具有更高的诊断精度和识别速度,对于少数测试样本进行诊断时证据冲突所带来的不确定性,利用D-S证据理论进行融合后得出的电堆健康状态诊断结论具有较高的信任度,不确定性明显降低,可为电堆的湿度软测量与优化控制提供便利。
最后,结合车用燃料电池系统离线和在线故障诊断与实际维护的需要,开发了一种嵌入式的手持故障诊断仪,设计了实用的故障码并提出了故障诊断策略与流程,此外,基于VC++编程语言设计了可视化的状态监测与故障诊断专家系统,完成了原型机基本功能的开发,诊断实例和结果证明了其实用性。
综上所述,本文分别从氢气泄漏、冗余多传感器的故障诊断与容错、电堆健康状态诊断以及状态监测与故障诊断系统设计这4个关键问题出发,提出了它们的研究方法和思路,相关计算和仿真结果验证了它们的可行性和有效性,可为今后进一步开展车用燃料电池系统故障诊断与维护所涉及到的其它问题研究提供一些参考。
As the dominating part of Fuel Cell Electric Vehicle(FCEV), atuomotive fuel cell system can hardly avoid malfunction, faults or even serious accidents for its complicated structure, atrocious operational circumstance and poor dynamic permformance, so it is important to study on its fault diagonosis to enhance the safety, relability, maintainability and market acceptance of both itself and FCEV. Based on the 60kW automotive fuel cell system designed by our group, in this paper, some key problems related to the safety and maintainability of the whole system, and the relability of its accessorial subsystems and fuel cell stack are studied. The main research works are as follows:
The common problems of automotive fuel cell system's fault characteristic and fault mechanism are put forward systemically, the fault characteristic is qualitatively analyzed from the points of coupling, gradual change, randomicity and uncertainty, and the fault mechanism is qualitatively analyzed from the aspects of mechanism factor, electric fator, device factors, human factors and environmental factors, based on the automotive fuel cell system designed, its faults are classified and ranked.
According to the hydrogen safety of the whole system, the fault tree analysis model of hydrogen leaking is established and qualitatively analyzed, its weakness is figured out, to evaluate the hydrogen safety, considering the uncertain fault probability of each elementary event, the fuzzy maths theory is combined into the traditional fault tree analysis method to quantitatively analyze the probability of top event and the importance degree of each elementary event, then some improvement measures and advice on hydrogen leaking are brought forward accordingly.
As to the fault dianosis and signals fault tolerant of multisensors in the accessorial systems, the united fault diagnosis and fault tolerant model based on two layer main and sub neural networks is set up, the difference between the predictive output of the main neural network and subnetworks with the sensors's practical output is constructed, these sensors are diagnosed to be in fault when the difference of the main network exceeds the threshold setted and lasts for the threshold steps time, and the output of certain sub network is used to replace the practical output of its corresponding sensor. Finally, the simulation is presented, it proves that the model set up can be effectively used to the fault diagosis and signals restoration of those sensors which have redundancey relationship, and it can meet the needs of the both temperature and air flow fault tolerant control of automotive fuel cell system.
Considering flooding and drying of fuel cell stack are difficult to be inerrably distinguished, an information fusion fault dignosis method combining binary tree support vector machine(SVM) and D-S evident theory is put forward, the fault characteristic is extracted from the real time process variables and their variational parameters feature which affect and reflect the water transmission in fuel cell stack, the output of binary tree SVM is utilized as the initial healthy state diagosis results, then they are fused with D-S evident theory to get the final fault dignosis conclusion. The simulation results reveal that, compared with neural networks it has preponderant precision and speed with binary tree SVM, and the belief degree is high and the uncertainty of the conclusion is largely lowered down when the conflicting results of SVMs are fused with D-S evident theory.
Finally, according to the requirement of off-line and online fault diagosis and maintenance, an embedded handheld fault diagosis instrument is designed, the diagnostic trouble code(DTC) is designed and the fualt diagnosis strategy and flow are provided. Besides, the visual state monitoring and fault diagnosis expert ssystem is designed with VC++ languange, the specific interfaces are realized, and the fault diagnosis example and its results prove their practicability.
Above all, hydrogen leaking, multi sensor fault diagnosis and tolerant, fuel cell stack healthy state diagnosis, the design of monitoring and fault diagnosis system are studied in this paper, the calculation and simulation results show the methods and ideas adopted are effective and practical, which can provide some reference to some other problems on its fault dignosis and maintenance in future.
引文
[1]江泽民.对中国能源问题的思考[J].上海交通大学学报,2008,42(3)：346-359.
[2]钱伯章.燃料电池发展前景及其应用(上)[J].节能环保,2007,(2)：23-25.
[3]钱伯章.燃料电池发展前景及其应用(下)[J].节能环保,2007,(4)：27-29.
[4]毛宗强.燃料电池[M].北京：化学工业出版社,2005：120-145.
[5]全书海,王超,宋娟.车用燃料电池系统控制系统与协调控制研究[J].华中师范大学学报(自然科学版),2005,39(3)：325-328.
[6]杨维,全书海,陈启宏.燃料电池堆单片电压巡检系统设计[J].武汉理工大学学报(信息与管理工程版),2007,29(2)：36-39.
[7]Daniel W, Steffen M H. Measuring individual cell voltages in fuel cell stacks[J]. Journal of Power Sources,2001,103(1):54-60.
[8]刘蒙,卢兰光,李中,等.基于TTCAN的燃料电池系统分布式控制系统设计[J].汽车工程,2006,28(9)：792-797.
[9]Lee H I, Lee C H, OH T Y, et al. Development of 1 kW class polymer electrolyte membrane fuel cell power generation system[J]. Journal of Power Sources,2002,107(1):110-119.
[10]Susai T, Kaneko M, Nakato K, et al. Optimization of proton exchange membranes and the humidifying conditions to improve cell performance for polymer electrolyte fuel cells [J]. International Journal of Hydrogen Energy,2001,26 (6):631-637.
[11]Katagiri T, Shimanuki H, Jusano Y, et al. Humidifier[P]. US:2001015500,2001-8-23.
[12]Hoagland L C. Desiccant-free heat and moisture exchange wheel[P]. US:01/95418, 2000-6-6.
[13]许思传,程钦等.燃料电池轿车用焓轮加湿器的设计[J].汽车技术,2006,(3)：18-21.
[14]Lutz A E, Larson R S, Keller J O. Thermodynamic comparison of fuel cells to the Carnot cycle[J]. International Journal of Hydrogen Energy,2002,27(10):1103-1111.
[15]全睿.燃料电池发动机故障诊断研究[D].武汉：武汉理工大学,2009.
[16]Li X G. Principles of Fuel Cells[M]. New York:Taylor&Francis Group,2006:1-300.
[17]Isermann R. Process Fault Detection Based on Modelling and Estimation Methods-A Survey[J]. Automatica,1984,20 (4):387-404.
[18]Frank P M. Fault Diagnosis in Dynamic System Using Analytical and Knowledge-based Reduneancy- A Survey and Some New Results[J]. Automatica,1990,26(3):459-474.
[19]邵庆龙,曹广益,朱新坚.质子交换膜燃料电池可靠性分析[J].能源技术,2003,24(4)： 145-151.
[20]Qi Z G, He C H, Arthur K. Effect of CO in the anode fuel on the perfornunce of PFM fuel cathod[J]. Journal of Power sources,2002,111(1):239-247.
[21]Di P D, Bencherif K, Sorine M. A Reduced Fuel Cell Stack Model for Control and Fault Diagnosis[J]. Journal of Fuel Cell Science and Technology,2006,3(4):384-388.
[22]Michael W F, Ronald F M, John C A, et al. Incorporation of voltage degradation into a generalised steady state electrochemical model for a PEM fulel cell[J]. Journal of Power Sources,2002,106(1):274-283.
[23]St-Pierre J, Wilkinson D P, Knights S, et al. Relationships between water management,contamination and lifetime degradation on PEMFC[J]. Journal of New Materials for Electrochemical Systems,2000,3(2):99-106.
[24]葛善海,衣宝廉,徐洪峰.质子交换膜燃料电池的研究[J].电化学,1998,4(3)：299-306.
[25]Quan R, Tan B H, Quan S H. Study on Fault Tree Analysis of Fuel Cell Stack Malfunction[C]. Proceedings of International Conference on Measuring Technology and Mechatronics Automation, Changsha, China,2010:579-582.
[26]Ingimundarson A, Stefanopoulou A G, McKay D A. Model-based detection of hydrogen leaks in a fuel cell stack[J]. IEEE Transactions on Control Systems Technology,2008,16(5): 1004-1012.
[27]赵奕磊,毛宗强,奚树人,等.基于故障树模型的燃料电池安全性评价[J].清华大学学报(自然科学版),2006,46(3)：425-428.
[28]Gerbec M, Jovan V, Petrovcic J. Operational and safety analyses of a commercial PEMFC system[J]. International Journal of Hydrogen Energy,2008,33(5):4147-4160.
[29]Watanabe S, Tamura Y, Suzuki J. The new facility for hydrogen and fuel cell vehicle safety evaluation[J]. International Journal of Hydrogen Energy,2007,32(13):2154-2161.
[30]Tian G Y, Wasterlain S, Endichi I, et al. Diagnosis methods dedicated to the localisation of failed cells within PEMFC stacks[J]. Journal of Power Sources,2008,182(2):449-461.
[31]王晓蕾,马建新,邬敏忠,等.燃料电池汽车的氢安全问题[J].中国科技论文在线,2008,3(5)：365-368.
[32]Malevich D, Halliop E, Peppley B A, et al. Effect of relative humidity on electrochemically active area and impedance response of PEM fuel cell[J]. ECS Transactions-Proton Exchange Membrane Fuel Cells,2008,16(2):1763-1774.
[33]Chen C, Fuller T F. The effect of humidity on the degradation of Nafion membrane[J]. Polymer Degradation and Stability,2009,94(9):1436-1447.
[34]Jiang J H, Yan W M, Li H Y, et al. Humidity of reactant fuel on the cell performance of PEM fuel cell with baffle-blocked flow field designs[J]. Journal of Power Sources,2006, 159(1):468-477.
[35]Liu Y, Murphy M W, Baker D R, et al. Proton conduction and oxygen reduction kinetics in PEM fuel cell cathodes: Effects of ionomer-to-carbon ratio and relative humidity[J], Journal of the Electrochemical Society,2009,156(8):B970-B980.
[36]Inoue G, Yoshimoto T, Matsukuma Y, et al. Numerical analysis of relative humidity distribution in polymer electrolyte fuel cell stack including cooling water[J]. Journal of Power Sources,2006,162(1):81-93.
[37]Hissel D, Pera M C, Kauffmann J M. Diagnosis of automotive fuel cell power generators [J]. Journal of Power Sources,2004,128(2):239-246.
[38]Narjiss A, Depernet D, Candusso D, et al. Online Diagnosis of PEM Fuel Cell[C]. Proceedings of 13th International Power Electronics and Motion Control Conference, Poznan, Poland,2008:734-735.
[39]Fouquet N, Doulet C, Nouillant C, et al. Model based PEM fuel cell state-of-health monitoring via ac impedance measurements [J]. Journal of Power Sources,2006,159(2): 905-913.
[40]Gebregergis A, Pillay P, Rengaswamy R. PEMFC Fault Diagnosis, Modeling,and Mitigation[J]. IEEE Transactions on Industry Applications,2010,46(1):295-303.
[41]裴普成,晁鹏翔,袁星.车用燃料电池在线故障诊断及处理方法[J].高技术通讯,2009,19(12)：1294-1298.
[42]郭家兴,朱新坚,曹广益.质子交换膜燃料电池故障诊断[J].电源技术,2008,32(8)：528-531.
[43]郭家兴.基于模糊模型的质子交换膜燃料电池的故障诊断[D].上海：上海交通大学,2008.
[44]陈启宏,舒芝锋,全书海.燃料电池内阻在线测试系统研究[J].华中师范大学学报(自然科学版),2007,41(30)：377-381.
[45]舒芝锋.燃料电池单片内阻在线测试与PEM含水量软测量研究[D].武汉：武汉理工大学,2008.
[46]陈启宏,宋娟,全书海.基于联合卡尔曼滤波器的燃料电池传感器故障检测与容错研究[J].华中师范大学学报(自然科学版),2006,40(1)：42-45.
[47]宋娟.燃料电池系统多传感器的故障诊断与容错技术研究[D].武汉：武汉理工大学,2006.
[48]张菊秀,全书海,王超.信息融合在燃料电池传感器故障识别中的应用[J].武汉理工大学学报,2006,28(6)：113-116.
[49]徐翠琴.基于信息融合技术的燃料电池多传感器故障检测研究[D].武汉：武汉理工大学，2007.
[50]白日光.燃料电池控制器及其故障诊断方法研究[D].上海：同济大学,2005.
[51]朱醒.基于参数估计的燃料电池轿车动力总成系统故障诊断方法研究[D].上海：同济大学，2007.
[52]杨红.FCV驱动系统故障诊断及容错控制算法研究[D].上海：同济大学,2009.
[53]Liu W, Wang X C. Fault diagnosis of a PEM fuel cell system by SP model and on-line expert systems techniques [C]. Proceedings of the IASTED International Conference on Modelling and Simulation, Banff, Canada,2003:188-191.
[54]Riascos L A M, Simoes M G, Miyagi P E. On-line fault diagnostic system for proton exchange membrane fuel cells[J]. Journal of Power Sources,2008,175(1):419-429.
[55]Riascos L A M, Simoes M G, Miyagi P E. A Bayesian network fault diagnostic system for proton exchange membrane fuel cells[J]. Journal of Power Sources,2007,165(1):267-278.
[56]Riascos Luis A M, Cozman F G, Miyagi P E, et al. Bayesian network supervision on fault tolerant fuel cells[C]. Proceedings of Conference Record-IAS Annual Meeting, Sao Paulo University, Brazil,2006:1059-1066.
[57]江明.室内燃气安全系统可靠性分析[J].平顶山工学院学报,2007,16(6)：37-41.
[58]Lees F P. Some data on the failure modes of instrument in the chemical plant environment[J]. Chemical Engineer,1973,277:418-421.
[59]周东华,叶银忠.现代故障诊断与容错控制[M].北京：清华大学出版社,2000：1-343.
[60]唐浩,屈梁生.基于支持向量机的发动机故障诊断[J].西安交通大学学报,2007,41(9)：1124-1126.
[61]Vahidi A, Stefanopoulou A, Peng H. Current management in a hybrid fuel cell power system: A model-predictive control approach [J]. IEEE Transactions on Control Systems Technology, 2006,14 (6):1047-1057.
[62]Arcak M, Gorgun H, Pedersen L M, et al. A nonlinear observer design for fuel cell hydrogen estimation[J]. IEEE Transactions on Control Systems Technology,2004,12(1):101-110.
[63]Pukrushpan J, Stefanopoulou A, Peng H. Control of natural gas catalytic partial oxidation for hydrogen generation in fuel cell aplications[J]. IEEE Transactions on Control Systems Technology,2005,13(1):3-14.
[64]Pukrushpan J P, Peng H, Anna G S. Control oriented modeling and analysis for automotive fuel cell systems[J]. Journal of Dynamic Systems, Measurement, and Control,2004,126 (1):14-25.
[65]Astrom K, Fontell E, Virtanen S. Reliability analysis and initial requirements for FC systems and stacks[J]. Journal of Power Sources,2007,171(1):46-54.
[66]Juan A, Sune Victor. An algorithm to find minimal cuts of coherent fault trees with event classes[J]. IEEE Transactions on reliability,1999,48(1):31-41.
[67]朱大奇.电子设备故障诊断原理与实践[M].北京：电子工业出版社,2004：73-92.
[68]刘绘珍,张力,王以群.故障树中最小割集和最小径集的改进算法[J].工业安全与环保,2006,32(4)：58-59.
[69]Yamashiro M. The median for a L-R fuzzy number[J]. Microelectron and Reliability,1995, 32(2):269-271.
[70]李兵,朱梅林,陈晓伟,等.模糊故障树分析法在内燃机可靠性中的应用研究[J].内燃机学报,1999,17(1)：63-66.
[71]赵艳萍,贡文伟.模糊故障树分析及其应用研究[J].中国安全科学学报,2001,11(6)：31-34.
[72]Singer D. Fuzzy set approach to fault tree and reliability analysis[J]. Fuzzy set and system,1990,34(2):145-155.
[73]刘国印,林少芬,江小霞,等.基于故障树的船舶起货机液压系统可靠性仿真分析[J].船海工程,2008,37(2)：70-73.
[74]龙志强,吕治国,常文森.基于模糊故障树的磁浮列车悬浮系统故障诊断[J].控制与决策,2004,19(2)：139-142.
[75]李锡江,刘荣,张厚祥,等.基于模糊故障树法的清洗机器人安全性研究[J].北京航空航天大学学报,2004,30(4)：344-348.
[76]李楠,刘琛.塔机电气系统的故障树分析[J].华北科技学院学报,2006,3(4)：69-71.
[77]蒋蓉,李楠.塔式起重机液压顶升系统的故障树分析[J].华北科技学院学报,2008,5(2)：51-52.
[78]Fussell J B, Vesely W E. A new methodology for obtaining cut sets for fault trees. Trans American Nuclear Socity,1972,15(6):262-263.
[79]Yamashiro M. The median for a L-R fuzzy number[J]. Microelectron and Reliability,1995, 32(2):269-271.
[80]李青,陆廷金.模糊重要度分析方法的研究[J].模糊系统与数学,2000,14(1)：89-93.
[81]王永传,郁文贤,庄钊文.一种故障树模糊重要度分析的新方法[J].国防科技大学学报,1999,21(3)：63-66.
[82]李冬辉,周巍巍.基于小波神经网络的传感器故障诊断方法研究[J].电工技术学报,2005,20(5)：49-52.
[83]高立娥,刘卫东,张萍,等.基于两级神经网络传感器故障诊断与容错控制技术[J].鱼 雷技术,2008,16(6)：34-37.
[84]张铭钧,孙瑞琛,王玉甲.基于RBF神经网络的水下机器人传感器状态监测方法研究[J].哈尔滨工程大学学报,2005,26(6)：726-731.
[85]Wu W, Feng G R, Li Z X, et al. Deterministic convergence of an online gradient method for BP neural networks[J]. IEEE Transactions on Neural Networks,2005,16(3):533-540.
[86]Hagan M H, Menhaj M B. Training feedforward networks with the Marquardt algorithm[J]. IEEE Transactions on Neural Networks,1994,6(5):989-993.
[87]Ngia L S H, joberg J S. Efficient training of neural nets for nonlinear adaptive filtering using a recursive Levenberg-Marquardt algorithm[J]. IEEE Transactions on Signal Processing, 2000,7(48):1915-1927.
[88]Gratton S, Lawless A S, Nichols N K. Approximate Gauss-newton methods for nonlinear least squares problems[J]. SIAM Journal on Optimization,2007,1(18):106-132.
[89]赵弘,周瑞祥,林廷圻.基于Levenberg2Marquardt算法的神经网络监督控制[J].西安交通大学学报,2002,36(5)：523-527.
[90]飞思科技产品研发中心.小波分析理论与MATLAB R2007实现[M].北京：电子工业出版社，2007：333-357.
[91]Kolmogorov A N. On the representation of continuous functions of many variables by superposition of continuous functions of one variable and addition[J]. Amer Math Soc,1963, 28(2):55-59.
[92]飞思科技产品研发中心.神经网络理论与MATLAB R2007实现[M].北京：电子工业出版社，2008：220-280.
[93]陈全世.先进电动汽车技术[M].北京：电子工业山版社,2007：145-148.
[94]Afshari E, Jazayeri S A. Analyses of heat and water transport interactions in a proton exchange membrane fuel cell[J]. Journal of Power Sources,2009,194(1):423-432.
[95]Lee D, Bae J. Evaluation of the net water transport through electrolytes in Proton Exchange Membrane Fuel Cell[J]. Journal of Power Sources,2009,191(2):390-399.
[96]Wang Z H, Wang C Y, Chen K S. Two-phase and transport in the air cathode of proton exchange membrane fuel cells[J]. Journal of Power Sources,2001,94(1):40-50.
[97]Burges C J C. A Tutorial on Support Vector Machines for Pattern Recognition [J]. Data Mining and Knowledge Discovery,1998,2(2):121-167.
[98]朱大奇,于盛林.基于D-S证据理论的数据融合算法及其在电路故障诊断中的应用[J].电子学报,2002,30(2)：221-224.
[99]Vapnik V N. The Nature of Statistical Learning Theory[M]. Springer: Verlag,1995:1-100.
[100]边肇祺.模式识别[M].北京：清华大学出版社(第2版),2000：284-304.
[101]Critianini N, Shawe T J. An introduction to support vector machines and other kernel based learning methods[M]. Cambridge: Cambridge University Press,2000:1-92.
[102]Hsu C W, LIN C J. A comparison of methods for multi-class support vector machines[J]. IEEE Transactions on Neural Networks,2002,13(2):415-425.
[103]夏建涛,何明一.支持向量机与纠错码相结合的多类分类算法[J].西北工业大学学报,2003,21(4)：444448.
[104]翟永杰,毛继佩,于丽敏,等.分级聚类支持向量机在汽轮机故障诊断中的应用[J].华北电力大学学报,2003,30(6)：25-29.
[105]Chen P, Liu S. An improved DAG-SVM for multi-class classification[C]. proceedings of 5th International Conference on Natural Computation, Tianjian, China,2009:460-462.
[106]Fei B, Liu J B. Binary tree of SVM: A new fast multiclass training and classification algorithm[J]. IEEE Transactions on Neural Networks,2006,17(3):696-704.
[107]何友,王国宏,陆大琻,等.多传感器信息融合及应用[M].北京：电子工业出版社(第2版),2000：96-131.
[108]陈小玄,罗大庸,单勇腾.基于神经网络信息融合的机车牵引电机故障诊断[M].计算机测量与控制,2007,15(5)：563-566.
[109]侯俊.证据理论的几个关键问题研究[D].西安：西北工业大学,2003：
[110]彭兵.基于改进支持向量机和特征信息融合的水电机组故障诊断[D].武汉：华中科技大学,2008：
[111]唐浩,屈梁生.基于支持向量机的发动机故障诊断[J].西安交通大学学报,2007,41(9)：1124-1125.
[112]孙永奎,陈光稿,李辉.基于支持向量机的模拟电路软故障诊断[J].计量学报,2009,30(3)：257-260：
[113]Wu T F, Lin C J, Weng R C. Probability estimates for multi-class classification by pairwise coupling[J]. Journal of Machine Learning Research,2004,5(1):975-1005.
[114]Kennedy J, Eberhart R. Particle swarm optimization[C]. Proceedings of the 1995 IEEE International Conference on Neural Networks, NJ, United States,1995:1942-1948.
[115]Zahara E, Hu C H. Solving constrained optimization problems with hybrid particle swarm optimization[J]. Engineering Optimization,2008,40(11):1031-1049.
[116]张树团,张晓赋,雷涛.基于粒子群算法和支持向量机的故障诊断研究[J].计算机测量与控制,2008,16(11)：1573-1574.
[117]魏秀业,潘宏伙,王福杰.基于粒子群优化的核主元分析特征的提取方法[J].振动、测 试与诊断,2009,29(2)：162-166.
[118]夏兴宇,王竹林,张自宾.基于支持向量机的控制系统故障诊断研究[J].中国测试,2009,35(11)：116-118.
[119]刘和平.通用数字信号控制器原理及应用-基于dsPIC30F系列[M].北京：北京航空航天大学出版社,2007：1406.
[120]张宏,詹德凯,林长加.基于CAN总线的汽车故障诊断系统研究与设计[J].汽车工程,2008,10(30)：934-937.
[121]张丽,方成,李建秋,等.基于ISO 15765的柴油机故障诊断接口开发[J].乍用发动机,2009,(4)：16-19.
[122]ISO 14230-1. Keyword Protocol 2000 Part 1:PhysicalLayer[S]. Genevese Switzerland: International Organization for Standardization,1999.
[123]Petersen E, Hesse K H, Kaess H, et al. New ABS with integral Automatic Traction Control for air-braked trucks and buses [J]. Society of Automotive Engineers Transactions,1990, 99(2):688-703.
[124]Anon. G.M.'s approach to SAE J1850:class 2[J]. Automotive Engineering International, 1991,99(3):37-42.
[125]全睿,黄亮,全书海.燃料电池汽车TTCAN网络的调度设计[J].武汉理工大学学报,2010,32(6)：71-75.
[126]Xie C J, Quan S H, Chen Q H. Multiple model control for hybrid power system of Fuel Cell Electric Vehicle[C].2008 IEEE Vehicle Power and Propulsion Conference, Harbin, China, 2008:978-982.
[127]吴友宇,谢长君,张成才.燃料电池电动车整车控制器研究[J].武汉理工大学学报,2004,26(5)：77-79.
[128]He Q, Li X Q. Management of knowledge base of expert system for fault diagnosis of rotating machinery[J]. Applied Mechanics and Materials,2011,44-47:2935-2939.
[129]Krishnamurthi M, Phillips D T. Expert system framework for machine fault diagnosis[J]. Computers and Industrial Engineering,1992,22(1):67-84.
[130]Shan X M, Yang H Y, Zhang P. Fault diagnosis expert system of artillery radar based on neural network [C]. Proceedings of 2010 International Conference on Computer Design and Applications, Qinhuangdao, China,2010:2426-2429.
[131]Liu J F, Wang W, Xiao Q G Fault diagnosis for flight control system of unmanned aerial vehicle using fuzzy petri nets[J]. ICIC Express Letters,2010,4(4):1319-1324.
[132]李十勇.模糊控制·神经控制和智能控制论[M].哈尔滨：哈尔滨工业大学出版社(第2版),2006：179-192.
[133]王弘,余化,黄雄武.制氧机组故障诊断专家系统知识库的研制[J].华中科技大学学报(自然科学版),2006,34(11)：91-92.
[134]邓正鹏,韦彩新,刘利娜,等.水电机组故障诊断专家系统中知识库的设计[J].华中科技大学学报(自然科学版),2003,31(9)：4-5.
[135]冯兰君,李建,吕炳朝.燃气轮机故障诊断专家系统的设计与实现[J].电子科技大学学报,2000,29(3)：278-281.
[136]魏传锋,李运泽,王浚,等.航天器热故障诊断专家系统推理机的设计[J].北京航空航天大学学报,2005,31(1)：61-62.
[137]张健,廖瑛,庄景钊.基于故障树分析法的某型直升机故障诊断专家系统设计分析[J].航空计算技术,2002,32(3)：76-78.
[138]孙鑫,余安萍.VC++深入详解[M].北京：电子工业出版社,2008：27-703.
[2]钱伯章.燃料电池发展前景及其应用(上)[J].节能环保,2007,(2)：23-25.
[3]钱伯章.燃料电池发展前景及其应用(下)[J].节能环保,2007,(4)：27-29.
[4]毛宗强.燃料电池[M].北京：化学工业出版社,2005：120-145.
[5]全书海,王超,宋娟.车用燃料电池系统控制系统与协调控制研究[J].华中师范大学学报(自然科学版),2005,39(3)：325-328.
[6]杨维,全书海,陈启宏.燃料电池堆单片电压巡检系统设计[J].武汉理工大学学报(信息与管理工程版),2007,29(2)：36-39.
[7]Daniel W, Steffen M H. Measuring individual cell voltages in fuel cell stacks[J]. Journal of Power Sources,2001,103(1):54-60.
[8]刘蒙,卢兰光,李中,等.基于TTCAN的燃料电池系统分布式控制系统设计[J].汽车工程,2006,28(9)：792-797.
[9]Lee H I, Lee C H, OH T Y, et al. Development of 1 kW class polymer electrolyte membrane fuel cell power generation system[J]. Journal of Power Sources,2002,107(1):110-119.
[10]Susai T, Kaneko M, Nakato K, et al. Optimization of proton exchange membranes and the humidifying conditions to improve cell performance for polymer electrolyte fuel cells [J]. International Journal of Hydrogen Energy,2001,26 (6):631-637.
[11]Katagiri T, Shimanuki H, Jusano Y, et al. Humidifier[P]. US:2001015500,2001-8-23.
[12]Hoagland L C. Desiccant-free heat and moisture exchange wheel[P]. US:01/95418, 2000-6-6.
[13]许思传,程钦等.燃料电池轿车用焓轮加湿器的设计[J].汽车技术,2006,(3)：18-21.
[14]Lutz A E, Larson R S, Keller J O. Thermodynamic comparison of fuel cells to the Carnot cycle[J]. International Journal of Hydrogen Energy,2002,27(10):1103-1111.
[15]全睿.燃料电池发动机故障诊断研究[D].武汉：武汉理工大学,2009.
[16]Li X G. Principles of Fuel Cells[M]. New York:Taylor&Francis Group,2006:1-300.
[17]Isermann R. Process Fault Detection Based on Modelling and Estimation Methods-A Survey[J]. Automatica,1984,20 (4):387-404.
[18]Frank P M. Fault Diagnosis in Dynamic System Using Analytical and Knowledge-based Reduneancy- A Survey and Some New Results[J]. Automatica,1990,26(3):459-474.
[19]邵庆龙,曹广益,朱新坚.质子交换膜燃料电池可靠性分析[J].能源技术,2003,24(4)： 145-151.
[20]Qi Z G, He C H, Arthur K. Effect of CO in the anode fuel on the perfornunce of PFM fuel cathod[J]. Journal of Power sources,2002,111(1):239-247.
[21]Di P D, Bencherif K, Sorine M. A Reduced Fuel Cell Stack Model for Control and Fault Diagnosis[J]. Journal of Fuel Cell Science and Technology,2006,3(4):384-388.
[22]Michael W F, Ronald F M, John C A, et al. Incorporation of voltage degradation into a generalised steady state electrochemical model for a PEM fulel cell[J]. Journal of Power Sources,2002,106(1):274-283.
[23]St-Pierre J, Wilkinson D P, Knights S, et al. Relationships between water management,contamination and lifetime degradation on PEMFC[J]. Journal of New Materials for Electrochemical Systems,2000,3(2):99-106.
[24]葛善海,衣宝廉,徐洪峰.质子交换膜燃料电池的研究[J].电化学,1998,4(3)：299-306.
[25]Quan R, Tan B H, Quan S H. Study on Fault Tree Analysis of Fuel Cell Stack Malfunction[C]. Proceedings of International Conference on Measuring Technology and Mechatronics Automation, Changsha, China,2010:579-582.
[26]Ingimundarson A, Stefanopoulou A G, McKay D A. Model-based detection of hydrogen leaks in a fuel cell stack[J]. IEEE Transactions on Control Systems Technology,2008,16(5): 1004-1012.
[27]赵奕磊,毛宗强,奚树人,等.基于故障树模型的燃料电池安全性评价[J].清华大学学报(自然科学版),2006,46(3)：425-428.
[28]Gerbec M, Jovan V, Petrovcic J. Operational and safety analyses of a commercial PEMFC system[J]. International Journal of Hydrogen Energy,2008,33(5):4147-4160.
[29]Watanabe S, Tamura Y, Suzuki J. The new facility for hydrogen and fuel cell vehicle safety evaluation[J]. International Journal of Hydrogen Energy,2007,32(13):2154-2161.
[30]Tian G Y, Wasterlain S, Endichi I, et al. Diagnosis methods dedicated to the localisation of failed cells within PEMFC stacks[J]. Journal of Power Sources,2008,182(2):449-461.
[31]王晓蕾,马建新,邬敏忠,等.燃料电池汽车的氢安全问题[J].中国科技论文在线,2008,3(5)：365-368.
[32]Malevich D, Halliop E, Peppley B A, et al. Effect of relative humidity on electrochemically active area and impedance response of PEM fuel cell[J]. ECS Transactions-Proton Exchange Membrane Fuel Cells,2008,16(2):1763-1774.
[33]Chen C, Fuller T F. The effect of humidity on the degradation of Nafion membrane[J]. Polymer Degradation and Stability,2009,94(9):1436-1447.
[34]Jiang J H, Yan W M, Li H Y, et al. Humidity of reactant fuel on the cell performance of PEM fuel cell with baffle-blocked flow field designs[J]. Journal of Power Sources,2006, 159(1):468-477.
[35]Liu Y, Murphy M W, Baker D R, et al. Proton conduction and oxygen reduction kinetics in PEM fuel cell cathodes: Effects of ionomer-to-carbon ratio and relative humidity[J], Journal of the Electrochemical Society,2009,156(8):B970-B980.
[36]Inoue G, Yoshimoto T, Matsukuma Y, et al. Numerical analysis of relative humidity distribution in polymer electrolyte fuel cell stack including cooling water[J]. Journal of Power Sources,2006,162(1):81-93.
[37]Hissel D, Pera M C, Kauffmann J M. Diagnosis of automotive fuel cell power generators [J]. Journal of Power Sources,2004,128(2):239-246.
[38]Narjiss A, Depernet D, Candusso D, et al. Online Diagnosis of PEM Fuel Cell[C]. Proceedings of 13th International Power Electronics and Motion Control Conference, Poznan, Poland,2008:734-735.
[39]Fouquet N, Doulet C, Nouillant C, et al. Model based PEM fuel cell state-of-health monitoring via ac impedance measurements [J]. Journal of Power Sources,2006,159(2): 905-913.
[40]Gebregergis A, Pillay P, Rengaswamy R. PEMFC Fault Diagnosis, Modeling,and Mitigation[J]. IEEE Transactions on Industry Applications,2010,46(1):295-303.
[41]裴普成,晁鹏翔,袁星.车用燃料电池在线故障诊断及处理方法[J].高技术通讯,2009,19(12)：1294-1298.
[42]郭家兴,朱新坚,曹广益.质子交换膜燃料电池故障诊断[J].电源技术,2008,32(8)：528-531.
[43]郭家兴.基于模糊模型的质子交换膜燃料电池的故障诊断[D].上海：上海交通大学,2008.
[44]陈启宏,舒芝锋,全书海.燃料电池内阻在线测试系统研究[J].华中师范大学学报(自然科学版),2007,41(30)：377-381.
[45]舒芝锋.燃料电池单片内阻在线测试与PEM含水量软测量研究[D].武汉：武汉理工大学,2008.
[46]陈启宏,宋娟,全书海.基于联合卡尔曼滤波器的燃料电池传感器故障检测与容错研究[J].华中师范大学学报(自然科学版),2006,40(1)：42-45.
[47]宋娟.燃料电池系统多传感器的故障诊断与容错技术研究[D].武汉：武汉理工大学,2006.
[48]张菊秀,全书海,王超.信息融合在燃料电池传感器故障识别中的应用[J].武汉理工大学学报,2006,28(6)：113-116.
[49]徐翠琴.基于信息融合技术的燃料电池多传感器故障检测研究[D].武汉：武汉理工大学，2007.
[50]白日光.燃料电池控制器及其故障诊断方法研究[D].上海：同济大学,2005.
[51]朱醒.基于参数估计的燃料电池轿车动力总成系统故障诊断方法研究[D].上海：同济大学，2007.
[52]杨红.FCV驱动系统故障诊断及容错控制算法研究[D].上海：同济大学,2009.
[53]Liu W, Wang X C. Fault diagnosis of a PEM fuel cell system by SP model and on-line expert systems techniques [C]. Proceedings of the IASTED International Conference on Modelling and Simulation, Banff, Canada,2003:188-191.
[54]Riascos L A M, Simoes M G, Miyagi P E. On-line fault diagnostic system for proton exchange membrane fuel cells[J]. Journal of Power Sources,2008,175(1):419-429.
[55]Riascos L A M, Simoes M G, Miyagi P E. A Bayesian network fault diagnostic system for proton exchange membrane fuel cells[J]. Journal of Power Sources,2007,165(1):267-278.
[56]Riascos Luis A M, Cozman F G, Miyagi P E, et al. Bayesian network supervision on fault tolerant fuel cells[C]. Proceedings of Conference Record-IAS Annual Meeting, Sao Paulo University, Brazil,2006:1059-1066.
[57]江明.室内燃气安全系统可靠性分析[J].平顶山工学院学报,2007,16(6)：37-41.
[58]Lees F P. Some data on the failure modes of instrument in the chemical plant environment[J]. Chemical Engineer,1973,277:418-421.
[59]周东华,叶银忠.现代故障诊断与容错控制[M].北京：清华大学出版社,2000：1-343.
[60]唐浩,屈梁生.基于支持向量机的发动机故障诊断[J].西安交通大学学报,2007,41(9)：1124-1126.
[61]Vahidi A, Stefanopoulou A, Peng H. Current management in a hybrid fuel cell power system: A model-predictive control approach [J]. IEEE Transactions on Control Systems Technology, 2006,14 (6):1047-1057.
[62]Arcak M, Gorgun H, Pedersen L M, et al. A nonlinear observer design for fuel cell hydrogen estimation[J]. IEEE Transactions on Control Systems Technology,2004,12(1):101-110.
[63]Pukrushpan J, Stefanopoulou A, Peng H. Control of natural gas catalytic partial oxidation for hydrogen generation in fuel cell aplications[J]. IEEE Transactions on Control Systems Technology,2005,13(1):3-14.
[64]Pukrushpan J P, Peng H, Anna G S. Control oriented modeling and analysis for automotive fuel cell systems[J]. Journal of Dynamic Systems, Measurement, and Control,2004,126 (1):14-25.
[65]Astrom K, Fontell E, Virtanen S. Reliability analysis and initial requirements for FC systems and stacks[J]. Journal of Power Sources,2007,171(1):46-54.
[66]Juan A, Sune Victor. An algorithm to find minimal cuts of coherent fault trees with event classes[J]. IEEE Transactions on reliability,1999,48(1):31-41.
[67]朱大奇.电子设备故障诊断原理与实践[M].北京：电子工业出版社,2004：73-92.
[68]刘绘珍,张力,王以群.故障树中最小割集和最小径集的改进算法[J].工业安全与环保,2006,32(4)：58-59.
[69]Yamashiro M. The median for a L-R fuzzy number[J]. Microelectron and Reliability,1995, 32(2):269-271.
[70]李兵,朱梅林,陈晓伟,等.模糊故障树分析法在内燃机可靠性中的应用研究[J].内燃机学报,1999,17(1)：63-66.
[71]赵艳萍,贡文伟.模糊故障树分析及其应用研究[J].中国安全科学学报,2001,11(6)：31-34.
[72]Singer D. Fuzzy set approach to fault tree and reliability analysis[J]. Fuzzy set and system,1990,34(2):145-155.
[73]刘国印,林少芬,江小霞,等.基于故障树的船舶起货机液压系统可靠性仿真分析[J].船海工程,2008,37(2)：70-73.
[74]龙志强,吕治国,常文森.基于模糊故障树的磁浮列车悬浮系统故障诊断[J].控制与决策,2004,19(2)：139-142.
[75]李锡江,刘荣,张厚祥,等.基于模糊故障树法的清洗机器人安全性研究[J].北京航空航天大学学报,2004,30(4)：344-348.
[76]李楠,刘琛.塔机电气系统的故障树分析[J].华北科技学院学报,2006,3(4)：69-71.
[77]蒋蓉,李楠.塔式起重机液压顶升系统的故障树分析[J].华北科技学院学报,2008,5(2)：51-52.
[78]Fussell J B, Vesely W E. A new methodology for obtaining cut sets for fault trees. Trans American Nuclear Socity,1972,15(6):262-263.
[79]Yamashiro M. The median for a L-R fuzzy number[J]. Microelectron and Reliability,1995, 32(2):269-271.
[80]李青,陆廷金.模糊重要度分析方法的研究[J].模糊系统与数学,2000,14(1)：89-93.
[81]王永传,郁文贤,庄钊文.一种故障树模糊重要度分析的新方法[J].国防科技大学学报,1999,21(3)：63-66.
[82]李冬辉,周巍巍.基于小波神经网络的传感器故障诊断方法研究[J].电工技术学报,2005,20(5)：49-52.
[83]高立娥,刘卫东,张萍,等.基于两级神经网络传感器故障诊断与容错控制技术[J].鱼 雷技术,2008,16(6)：34-37.
[84]张铭钧,孙瑞琛,王玉甲.基于RBF神经网络的水下机器人传感器状态监测方法研究[J].哈尔滨工程大学学报,2005,26(6)：726-731.
[85]Wu W, Feng G R, Li Z X, et al. Deterministic convergence of an online gradient method for BP neural networks[J]. IEEE Transactions on Neural Networks,2005,16(3):533-540.
[86]Hagan M H, Menhaj M B. Training feedforward networks with the Marquardt algorithm[J]. IEEE Transactions on Neural Networks,1994,6(5):989-993.
[87]Ngia L S H, joberg J S. Efficient training of neural nets for nonlinear adaptive filtering using a recursive Levenberg-Marquardt algorithm[J]. IEEE Transactions on Signal Processing, 2000,7(48):1915-1927.
[88]Gratton S, Lawless A S, Nichols N K. Approximate Gauss-newton methods for nonlinear least squares problems[J]. SIAM Journal on Optimization,2007,1(18):106-132.
[89]赵弘,周瑞祥,林廷圻.基于Levenberg2Marquardt算法的神经网络监督控制[J].西安交通大学学报,2002,36(5)：523-527.
[90]飞思科技产品研发中心.小波分析理论与MATLAB R2007实现[M].北京：电子工业出版社，2007：333-357.
[91]Kolmogorov A N. On the representation of continuous functions of many variables by superposition of continuous functions of one variable and addition[J]. Amer Math Soc,1963, 28(2):55-59.
[92]飞思科技产品研发中心.神经网络理论与MATLAB R2007实现[M].北京：电子工业出版社，2008：220-280.
[93]陈全世.先进电动汽车技术[M].北京：电子工业山版社,2007：145-148.
[94]Afshari E, Jazayeri S A. Analyses of heat and water transport interactions in a proton exchange membrane fuel cell[J]. Journal of Power Sources,2009,194(1):423-432.
[95]Lee D, Bae J. Evaluation of the net water transport through electrolytes in Proton Exchange Membrane Fuel Cell[J]. Journal of Power Sources,2009,191(2):390-399.
[96]Wang Z H, Wang C Y, Chen K S. Two-phase and transport in the air cathode of proton exchange membrane fuel cells[J]. Journal of Power Sources,2001,94(1):40-50.
[97]Burges C J C. A Tutorial on Support Vector Machines for Pattern Recognition [J]. Data Mining and Knowledge Discovery,1998,2(2):121-167.
[98]朱大奇,于盛林.基于D-S证据理论的数据融合算法及其在电路故障诊断中的应用[J].电子学报,2002,30(2)：221-224.
[99]Vapnik V N. The Nature of Statistical Learning Theory[M]. Springer: Verlag,1995:1-100.
[100]边肇祺.模式识别[M].北京：清华大学出版社(第2版),2000：284-304.
[101]Critianini N, Shawe T J. An introduction to support vector machines and other kernel based learning methods[M]. Cambridge: Cambridge University Press,2000:1-92.
[102]Hsu C W, LIN C J. A comparison of methods for multi-class support vector machines[J]. IEEE Transactions on Neural Networks,2002,13(2):415-425.
[103]夏建涛,何明一.支持向量机与纠错码相结合的多类分类算法[J].西北工业大学学报,2003,21(4)：444448.
[104]翟永杰,毛继佩,于丽敏,等.分级聚类支持向量机在汽轮机故障诊断中的应用[J].华北电力大学学报,2003,30(6)：25-29.
[105]Chen P, Liu S. An improved DAG-SVM for multi-class classification[C]. proceedings of 5th International Conference on Natural Computation, Tianjian, China,2009:460-462.
[106]Fei B, Liu J B. Binary tree of SVM: A new fast multiclass training and classification algorithm[J]. IEEE Transactions on Neural Networks,2006,17(3):696-704.
[107]何友,王国宏,陆大琻,等.多传感器信息融合及应用[M].北京：电子工业出版社(第2版),2000：96-131.
[108]陈小玄,罗大庸,单勇腾.基于神经网络信息融合的机车牵引电机故障诊断[M].计算机测量与控制,2007,15(5)：563-566.
[109]侯俊.证据理论的几个关键问题研究[D].西安：西北工业大学,2003：
[110]彭兵.基于改进支持向量机和特征信息融合的水电机组故障诊断[D].武汉：华中科技大学,2008：
[111]唐浩,屈梁生.基于支持向量机的发动机故障诊断[J].西安交通大学学报,2007,41(9)：1124-1125.
[112]孙永奎,陈光稿,李辉.基于支持向量机的模拟电路软故障诊断[J].计量学报,2009,30(3)：257-260：
[113]Wu T F, Lin C J, Weng R C. Probability estimates for multi-class classification by pairwise coupling[J]. Journal of Machine Learning Research,2004,5(1):975-1005.
[114]Kennedy J, Eberhart R. Particle swarm optimization[C]. Proceedings of the 1995 IEEE International Conference on Neural Networks, NJ, United States,1995:1942-1948.
[115]Zahara E, Hu C H. Solving constrained optimization problems with hybrid particle swarm optimization[J]. Engineering Optimization,2008,40(11):1031-1049.
[116]张树团,张晓赋,雷涛.基于粒子群算法和支持向量机的故障诊断研究[J].计算机测量与控制,2008,16(11)：1573-1574.
[117]魏秀业,潘宏伙,王福杰.基于粒子群优化的核主元分析特征的提取方法[J].振动、测 试与诊断,2009,29(2)：162-166.
[118]夏兴宇,王竹林,张自宾.基于支持向量机的控制系统故障诊断研究[J].中国测试,2009,35(11)：116-118.
[119]刘和平.通用数字信号控制器原理及应用-基于dsPIC30F系列[M].北京：北京航空航天大学出版社,2007：1406.
[120]张宏,詹德凯,林长加.基于CAN总线的汽车故障诊断系统研究与设计[J].汽车工程,2008,10(30)：934-937.
[121]张丽,方成,李建秋,等.基于ISO 15765的柴油机故障诊断接口开发[J].乍用发动机,2009,(4)：16-19.
[122]ISO 14230-1. Keyword Protocol 2000 Part 1:PhysicalLayer[S]. Genevese Switzerland: International Organization for Standardization,1999.
[123]Petersen E, Hesse K H, Kaess H, et al. New ABS with integral Automatic Traction Control for air-braked trucks and buses [J]. Society of Automotive Engineers Transactions,1990, 99(2):688-703.
[124]Anon. G.M.'s approach to SAE J1850:class 2[J]. Automotive Engineering International, 1991,99(3):37-42.
[125]全睿,黄亮,全书海.燃料电池汽车TTCAN网络的调度设计[J].武汉理工大学学报,2010,32(6)：71-75.
[126]Xie C J, Quan S H, Chen Q H. Multiple model control for hybrid power system of Fuel Cell Electric Vehicle[C].2008 IEEE Vehicle Power and Propulsion Conference, Harbin, China, 2008:978-982.
[127]吴友宇,谢长君,张成才.燃料电池电动车整车控制器研究[J].武汉理工大学学报,2004,26(5)：77-79.
[128]He Q, Li X Q. Management of knowledge base of expert system for fault diagnosis of rotating machinery[J]. Applied Mechanics and Materials,2011,44-47:2935-2939.
[129]Krishnamurthi M, Phillips D T. Expert system framework for machine fault diagnosis[J]. Computers and Industrial Engineering,1992,22(1):67-84.
[130]Shan X M, Yang H Y, Zhang P. Fault diagnosis expert system of artillery radar based on neural network [C]. Proceedings of 2010 International Conference on Computer Design and Applications, Qinhuangdao, China,2010:2426-2429.
[131]Liu J F, Wang W, Xiao Q G Fault diagnosis for flight control system of unmanned aerial vehicle using fuzzy petri nets[J]. ICIC Express Letters,2010,4(4):1319-1324.
[132]李十勇.模糊控制·神经控制和智能控制论[M].哈尔滨：哈尔滨工业大学出版社(第2版),2006：179-192.
[133]王弘,余化,黄雄武.制氧机组故障诊断专家系统知识库的研制[J].华中科技大学学报(自然科学版),2006,34(11)：91-92.
[134]邓正鹏,韦彩新,刘利娜,等.水电机组故障诊断专家系统中知识库的设计[J].华中科技大学学报(自然科学版),2003,31(9)：4-5.
[135]冯兰君,李建,吕炳朝.燃气轮机故障诊断专家系统的设计与实现[J].电子科技大学学报,2000,29(3)：278-281.
[136]魏传锋,李运泽,王浚,等.航天器热故障诊断专家系统推理机的设计[J].北京航空航天大学学报,2005,31(1)：61-62.
[137]张健,廖瑛,庄景钊.基于故障树分析法的某型直升机故障诊断专家系统设计分析[J].航空计算技术,2002,32(3)：76-78.
[138]孙鑫,余安萍.VC++深入详解[M].北京：电子工业出版社,2008：27-703.