基于人工免疫系统的异常状态监测及故障诊断研究
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摘要
复杂装备系统状态监测及故障诊断存在故障先验知识缺乏、状态监测依赖于参数阈值的设定以及监测与诊断分离等问题。根据生物免疫系统智能机理发展起来的人工免疫系统为解决上述问题提供了新思路。本论文围绕人工免疫系统反面选择算法中的核心问题——检测器生成过程中存在“孔洞”和冗余的情况，提出了超环检测器及广义超环检测器等概念，研究了建立在超环检测器及广义超环检测器基础上的设备异常监测方法。提出了基于抗原-抗体免疫识别机理的模式识别方法，继而建立了异常状态监测与故障诊断一体化的快速反应机制。
针对人工免疫系统反面选择算法所存在的主要问题，提出了超环检测器新概念，解决了设备异常状态监测过程中设备异常等级及异常度的描述问题，为设备异常度监测提供了一种新方法。
针对所提出的超环检测器，分析了超环检测器产生的策略，提出了基于aiNet免疫网络的约简记忆中心超环检测器构造方法，有效解决了传统检测器“孔洞”与冗余的突出矛盾，提高了异常监测速度和效率，同时实现了设备异常等级监测，通过标准数据和轴承监测实例，验证了方法的可行性及有效性。
依据超环检测器概念及支持向量数据描述方法，提出了进一步提高设备异常度监测效率的广义超环检测器概念，提出了广义超环检测器构造方法及异常度监测方法，研究成果经过了标准数据及轴承故障数据检验。
针对复杂设备状态监测与故障诊断分离的问题，根据所构建的广义超环检测器将非己空间划分成具有不同异常等级的异常子空间，为了将异常子空间与故障子空间产生联系，实现状态监测与故障诊断一体化，提出了抗原-抗体免疫识别机理模式识别方法，通过UCI国际标准数据库中的Iris鸢尾花数据与Wine数据验证了模式识别方法的有效性，准确的模式识别方法为监测与诊断的快速联系提供了保障。
There are some problems in state monitoring and fault diagnosis of complexequipment, such as lack of fault prior knowledge, state monitoring depend onthreshold of parameters and monitoring and diagnosis are separate from each other.Artificial Immune System is inspired by intelligent mechanisms of the BiologicalImmune System which gives us some new ideas. This dissertation focuses on thecore problem of Negative Selection Algorithm: holes or redundancy in the process ofdetector generation. To solve the core problem, the concept of hyper-ring detectorand its generalized form are proposed. Anomaly detection methods which based onhyper-ring detector and generalized hyper-ring detector are discussed. A patternrecognition method based on antigen-antibody recognition mechanism is proposed,and then quick response mechnism between state monitoring and fault diagnosis areset up.
To solve the main problem in Negative Selection Algorithm, a new concept ofhyper-ring detector is proposed. The description problem of anomaly degree andlevel of anomaly in anomaly state monitoring is solved, which provide a novelmethod in anomaly degree monitoring of equipment.
Generation strategies of hyper-ring detector are analysed, the constructionmethod of reduction memory center hyper-ring detector is proposed based on aiNetimmune network which solved conflict between holes and redundancy, the efficiencyof anomaly detection are greatly increased and the level of anomaly is achieved atthe same time. The efficiency and feasibility of proposed reduction memory centerhyper-ring detector is validated by standard data and bearing fault data.
Based on the concept of hyper-ring detector and support vector data descriptionmethod, a more efficient generalized hyper-ring detector is proposed and also itsconstruction method and anomaly degree detection method. The research results areverified by standard data and bearing fault data.
To solve monitoring and diagnosis separated problem, the non-self space isdivided into several anomaly subspaces based on generalized hyper-ring detector. Torealize integration between monitoring and diagnosis, a novel pattern recognitionmethod based on antigen-antibody recognition mechanism is proposed, and itsadvantage are validated by Iris and Wine data from UCI database. The proposedpattern recognition method provide guarantee in quick response between statemonitoring and fault diagnosis.
针对人工免疫系统反面选择算法所存在的主要问题，提出了超环检测器新概念，解决了设备异常状态监测过程中设备异常等级及异常度的描述问题，为设备异常度监测提供了一种新方法。
针对所提出的超环检测器，分析了超环检测器产生的策略，提出了基于aiNet免疫网络的约简记忆中心超环检测器构造方法，有效解决了传统检测器“孔洞”与冗余的突出矛盾，提高了异常监测速度和效率，同时实现了设备异常等级监测，通过标准数据和轴承监测实例，验证了方法的可行性及有效性。
依据超环检测器概念及支持向量数据描述方法，提出了进一步提高设备异常度监测效率的广义超环检测器概念，提出了广义超环检测器构造方法及异常度监测方法，研究成果经过了标准数据及轴承故障数据检验。
针对复杂设备状态监测与故障诊断分离的问题，根据所构建的广义超环检测器将非己空间划分成具有不同异常等级的异常子空间，为了将异常子空间与故障子空间产生联系，实现状态监测与故障诊断一体化，提出了抗原-抗体免疫识别机理模式识别方法，通过UCI国际标准数据库中的Iris鸢尾花数据与Wine数据验证了模式识别方法的有效性，准确的模式识别方法为监测与诊断的快速联系提供了保障。
There are some problems in state monitoring and fault diagnosis of complexequipment, such as lack of fault prior knowledge, state monitoring depend onthreshold of parameters and monitoring and diagnosis are separate from each other.Artificial Immune System is inspired by intelligent mechanisms of the BiologicalImmune System which gives us some new ideas. This dissertation focuses on thecore problem of Negative Selection Algorithm: holes or redundancy in the process ofdetector generation. To solve the core problem, the concept of hyper-ring detectorand its generalized form are proposed. Anomaly detection methods which based onhyper-ring detector and generalized hyper-ring detector are discussed. A patternrecognition method based on antigen-antibody recognition mechanism is proposed,and then quick response mechnism between state monitoring and fault diagnosis areset up.
To solve the main problem in Negative Selection Algorithm, a new concept ofhyper-ring detector is proposed. The description problem of anomaly degree andlevel of anomaly in anomaly state monitoring is solved, which provide a novelmethod in anomaly degree monitoring of equipment.
Generation strategies of hyper-ring detector are analysed, the constructionmethod of reduction memory center hyper-ring detector is proposed based on aiNetimmune network which solved conflict between holes and redundancy, the efficiencyof anomaly detection are greatly increased and the level of anomaly is achieved atthe same time. The efficiency and feasibility of proposed reduction memory centerhyper-ring detector is validated by standard data and bearing fault data.
Based on the concept of hyper-ring detector and support vector data descriptionmethod, a more efficient generalized hyper-ring detector is proposed and also itsconstruction method and anomaly degree detection method. The research results areverified by standard data and bearing fault data.
To solve monitoring and diagnosis separated problem, the non-self space isdivided into several anomaly subspaces based on generalized hyper-ring detector. Torealize integration between monitoring and diagnosis, a novel pattern recognitionmethod based on antigen-antibody recognition mechanism is proposed, and itsadvantage are validated by Iris and Wine data from UCI database. The proposedpattern recognition method provide guarantee in quick response between statemonitoring and fault diagnosis.
引文
[1].新华网.辽阳石化公司发生爆炸事故.http://119.china.com.cn/jdxw/txt/2008-09/14/content_2472818.htm,2008.
[2].百度文库.往复式压缩机培训课件.http://wenku.baidu.com/view/96ff47afd1f34693daef3e12.html,2009.
[3].央视网.湖北长阳一家化工厂因机器故障引爆.http://news.cntv.cn/china/20120623/102644.shtml,2012.
[4].高金吉.机器故障诊治与自愈化[M].北京:高等教育出版社,2012.
[5].陈予恕.机械故障诊断的非线性动力学原理[J].机械工程学报,2007,43(1):25-34.
[6].韩清凯,于晓光.基于振动分析的现代机械故障诊断原理及应用[M].北京:科学出版社,2010.
[7]. P.M.Frank. Analytical and Qualitative Model Based Fault Diagnosis-A Survey and SomeNew Results[J]. European J.Control,2001,5(2):6-8.
[8].闻新,张洪钱,周露.控制系统的故障诊断和容错控制[M].北京:机械工业出版社,1998.
[9].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.
[10]. R.J.Patton, J.Chen, H.Benkhedda. A study on neuro-fuzzy systems for fault diagnosis[J].International Journal of Systems Science,2000,31(11):1441-1448.
[11]. Rolf Isermann. Process Fault Detection Based on Modeling and Estimation Methods-ASurvey[J]. Automatica,1984,20(4):387-404.
[12].于百胜,黄文虎.基于强跟踪滤波器的在线故障诊断方法[J].振动、测试与诊断,2003,23(4):260-263.
[13].姚红良,李鹤,李小彭等.裂纹碰摩耦合故障转子系统诊断分析[J].振动工程学报,2006,19(3):307-311.
[14].万方义,许庆余,宋笔锋等.非线性裂纹转子系统碰摩故障参变特性研究[J].机械强度,2006,28(5):633-638.
[15].林言丽,褚福磊.裂纹转子的刚度模型[J].机械工程学报,2008,44(1):114-120.
[16].孙保苍.动态油膜-裂纹转子系统非线性行为研究[J].农业机械学报,2004,35(5):170-173.
[17].郭保良,段志善,郑建校等.振动机械滚动轴承单点点蚀故障诊断研究[J].振动工程学报,2012,25(5):610-618.
[18].马辉,孙伟,任朝晖等.多盘悬臂转子系统制作松动故障研究[J].航空动力学报,2009,24(7):1512-1517.
[19].从云飞,陈进,董光明.基于滚动轴承故障诊断的AR预测滤波器阶数问题研究[J].振动与冲击,2012,31(4):44-47.
[20].李志农,何永勇,吴昭同等. ARMA模型盲辨识仿真研究及其在机械故障诊断中的应用[J].振动与冲击,2006,25(1):122-125.
[21].李志农,曾明如,韩捷等.主分量分析和因子隐Markov模型在机械故障诊断中的应用[J].机械强度,2007,29(1):25-29.
[22].李志农,郝伟,韩捷等.基于非线性时序模型盲辨识的因子隐Markov模型识别方法[J].机械工程学报,2007,43(1):191-195.
[23].于德介,陈淼峰,程军圣等.基于AR模型和支持向量机的转子系统故障诊断方法[J].系统工程理论与实践,2007,(5):152-156.
[24].马晋,江志农,高金吉.基于活塞杆轴心位置轨迹的往复压缩机故障诊断技术研究[J].振动工程学报,2012,25(4):453-459.
[25].褚福磊,彭志科,冯志鹏等.机械故障诊断中现代信号处理方法[M].北京:科学出版社,2009.
[26].王小玲,陈进,从云飞.基于时频的频带熵方法在滚动轴承故障识别中的应用[J].振动与冲击,2012,31(18):29-33.
[27].明阳,陈进.基于谱相关密度切片分析和SVM的滚动轴承故障诊断[J].振动与冲击,2010,29(1):196-199.
[28].董广明,陈进.基于循环频率能量的滚动轴承损伤程度识别[J].振动工程学报,2010,23(3):249-253.
[29].张进,冯志鹏,褚福磊.滚动轴承故障特征的时间-小波能量谱提取方法[J].机械工程学报,2011,47(17):44-49.
[30].明安波,褚福磊,张炜.滚动轴承故障特征提取的频谱自相关方法[J].机械工程学报,2012,48(19):65-70.
[31].任达千,杨世锡,吴昭同等. LMD时频分析方法的端点效应在旋转机械故障诊断中的影响[J].中国机械工程,2012,23(8):951-956.
[32].任达千,杨世锡,吴昭同等.信号瞬时频率直接计算法与Hilbert变换、Teager能量法比较[J].机械工程学报,2013,49(2).
[33].李凌均,韩捷,李朋勇等.矢双谱分析及其在机械故障诊断中的应用[J].机械工程学报,2011,47(17):50-54.
[34].王晓冬,何正嘉,訾艳阳.多小波自适应构造方法及滚动轴承复合故障诊断研究[J].振动工程学报,2010,23(4):438-443.
[35].王衍学,何正嘉,訾艳阳等.基于LMD的时频分析方法及其机械故障诊断应用研究[J].振动与冲击,2012,31(9):9-12.
[36].陈雪峰,向家伟,董洪波等.基于区间B样条小波有限元的转子裂纹定量识别[J].机械工程学报,2007,43(3):123-127.
[37].李常有,徐敏强,郭耸.基于改进的Hilbert-Huang变换的滚动轴承故障诊断[J].振动与冲击,2007,26(4):39-41.
[38].高清清,贾民平.基于EEMD的奇异谱熵在旋转机械故障诊断中的应用[J].东南大学学报(自然科学版),2011,41(5):998-1001.
[39].陈亚农,郜普刚,何田等.局部均值分解在滚动轴承故障综合诊断中的应用[J].振动与冲击,2012,31(3):73-78.
[40].毛永芳,秦树人,秦毅.重分配谱图和多窗谱在机械故障诊断中的应用[J].振动与冲击,2009,28(1):161-164.
[41].秦毅,秦树人,毛永芳.基于小波脊线的解调方法及其在旋转机械故障诊断中的应用[J].机械工程学报,2009,45(2):231-237.
[42].郝研,王太勇,万剑等.基于经验模式分解和广义维数的机械故障诊断[J].机械工程学报,2012,42(2):392-396.
[43].王太勇,何慧龙,王国锋等.基于经验模式分解和最小二乘支持矢量机的滚动轴承故障诊断[J].机械工程学报,2007,43(4):88-92.
[44].沈国际,陶利民,温熙森等.基于Wigner分布的齿轮箱振动信号相位估计[J].机械工程学报,2004,40(9):185-189.
[45].曹冲锋,杨世锡,杨将新等.基于双谱特征量的滚动轴承损伤部位识别[J].农业机械学报,2007,38(9):179-182.
[46].来五星,轩建平,史铁林等. Wigner-Ville时频分布研究及其在齿轮故障诊断中的应用[J].振动工程学报,2003,16(2):247-250.
[47].于德介,程军圣,杨宇. Hilbert-Huang变换在齿轮故障诊断中的应用[J].湖南大学学报（自然科学版）,2005,41(6):102-106.
[48].罗洁思,于德介,彭富强.基于EMD的多尺度形态学解调方法及其在机械故障诊断中的应用[J].振动与冲击,2009,28(11):84-86.
[49].程军圣,郑近德,杨宇.基于局部特征尺度分解的经验包络解调方法及其在机械故障诊断中的应用[J].机械工程学报,2012,48(19):87-94.
[50].吴军强,梁军.基于图论的故障诊断技术及其发展[J].机电工程,2003,20(5):188-190.
[51].闻竞竞,黄道.故障诊断方法综述[J].计算机技术与应用展望,2007:1306-1310.
[52].徐东,徐永成,陈循等.基于EMD的灰色模型的疲劳剩余寿命预测方法研究[J].振动工程学报,2011,24(1):104-110.
[53]. Mariela Cerrada, Juan Cardillo, Jose Aguilar and Raúl Faneite. Agents-based design forfault management systems in industrial processes[J]. Computers in Industry.2007,58(4):313-328.
[54]. Gang Niu, Tian Han, Bo-Suk Yang and Andy Chit Chiow Tan. Multi-agent decisionfusion for motor fault diagnosis[J]. Mechanical Systems and Signal Processing.2007,21(3):1285-1299.
[55].宋其江,徐敏强,王日新.基于MAS的航天器故障诊断系统模型研究[J].吉林大学学报工学版,2009,39(2):546-550.
[56]. Zhang S, Mathew J, Ma L, et al. Best basis-based intelligent machine fault diagnosis[J].Mechanical Systems and Signal Processing,2005,19(2):357-370.
[57]. Sampath S, Singh R. An integrated fault diagnostics model using genetic algorithm andneural networks[J]. Transactions of the ASME Journal of Engineering for Gas Turbinesand Power,2006,128(1):49-56.
[58]. Li RQ, Chen J, Wu X, et al. Fault diagnosis of rotating machinery based on SVD, FCMand RST[J]. International Journal of Advanced Manufacturing Technology,2005,27(1-2):128-135.
[59].雷亚国,何正嘉,訾艳阳.基于混合智能新模型的故障诊断[J].机械工程学报,2008,44(7):112-117.
[60]. L.N.D. Castro, F.J.V. Zuben, The clonal selection algorithm with engineeringapplications[C], Genetic and Evolutionary Computation Conference (GECCO’00)–Workshop Proceedings, Las Vegas, Nevada, USA,2000.
[61]. De Castro L. N., Von Zuben F. J. Immune and Neural Network Models: Theoretial andEmpirical Comparisons[J]. International Journal of Computational Intelligence andApplications (IJCIA),2001,1(3):239-257.
[62]. Thiago A. S. Masutti, L. N. De Castro. A Self-organizing Neural Network Using Ideasfrom the Immune System to Solve the Traveling Salesman Problem[J]. InformationSciences,2009,179(10):1454-1468.
[63]. Thiago A. S. Masutti, L. N. De Castro. Neuro-immune Approach to Solve RoutingProblems[J]. Neurocomputing,2009,72(10-12):2189-2197.
[64]. A. Ciccazzo, P. Conca, G.. Nicosia, G. Stracquadanio, An advanced clonal selectionalgorithm with ad hoc network-based hypermutation operators for synthesis of topologyand sizing of analog electrical circuits[C], in:7th International Conference on ArtificialImmune Systems, Phuket, Thailand,2008.
[65]. R. Halavati, S.B. Shouraki, M.J. Heravi, B.J. Jashmi, An artificial immune system withpartially specified antibodies[C], in:9th annual conference on Genetic and evolutionarycomputation (GECCO2007), London, England,2007.
[66]. P. May, J. Timmis, K. Mander, Immune and evolutionary approaches to softwaremutation testing[C], in:6th International Conference on Artificial Immune Systems(ICARIS2007), Santos, Brazil,2007.
[67].张向荣,焦李成.基于免疫克隆选择算法的特征选择[J].复旦学报,2004,43(5):926-929.
[68].戚玉涛,刘芳,焦李成.基于分布式人工免疫算法的数值优化[J].电子学报,2009,37(7):1554-1561.
[69].尚荣华,焦李成,马文萍.免疫克隆多目标优化算法求解约束优化问题[J].软件学报,2008, pp:2943-2956.
[70].杨咚咚,焦李成,公茂果等.求解偏好多目标优化的克隆选择算法[J].软件学报,2010,21(1):14-33.
[71].戚玉涛,刘芳,焦李成.求解大规模TSP问题的自适应归约免疫算法[J].软件学报,2008,19(6):1265-1273.
[72].戚玉涛,刘芳,焦李成.求解TSP问题免疫算法的动态疫苗策略[J].西安电子科技大学学报,2008,35(1):37-42.
[73].戚玉涛,焦李成,刘芳.基于并行人工免疫算法的大规模TSP问题求解[J].电子学报,2008,36(8):1552-1558.
[74].黄席樾,张著洪,何传江等.现代智能算法理论及应用[M].北京:科学出版社,2005.
[75].戴健,杨宏晖,杜方键等.一种用于风机故障诊断的免疫克隆特征选择算法[J].声学技术,2012,31(6):593-596.
[76].薛晋东,王琦.一种基于克隆选择算法的变压器故障诊断方法[J].电力学报,2012,27(2):123-126.
[77].侯胜利,王威,乔丽等.基于克隆聚类的特征提取与多传感器故障诊断[J].电光与控制,2010,17(6):69-72.
[78]. Jon Timmis, Mark Neal, John Hunt. An artificial immune system for data analysis[J],BioSystems,2000,55:143-150.
[79]. J. Kim, P.J. Bentley, U. Aickelin and Julie Greensmith et al. Immune system approachesto intrusion detection-a review[J], Natural Computing,2007,6:413-466.
[80]. Perelson A. Immune network theory[J]. Immunological Review,1989,110:5-36.
[81]. Ishiguro A, Shirai Y, Kondo T,et al. Immunoid: an architecture for behavior arbitrationbased on the immune networks//Proc. of IEEE/RSJ International Conference onIntelligent Robots and Systems, Osaka, Japan,1996:1730-1738.
[82]. Yanfei Zhong, Liangpei Zhang, Jianya Gong and Pingxiang Li. A Supervised ArtificialImmune Classifier for Remote-Sensing Imagery[J]. IEEE transactions on geoscience andremote sensing,2007,45(12):3957-3966.
[83]. De Castro L N, Von Zuben F J. Artificial immune system: Part I:basic theory andapplication. School of Electrical and Computer Engineering, State University ofCampinas, Campinas-SR, Brazil: Technical Report RT-DCA01,1999.
[84]. Ishida Y, Adachi N. Active Noise Control by an Immune Algorithm: Adaptation ImmuneSystem as an Evolution[C]. In Proc.1996IEEE Int Conf on Evolutionary Compuation,Nagoya,1996:150-153.
[85]. Hunt J. E., Cooke D. E. Learning Using An Artificial Immune System[J]. Journal ofNetwork and Computer Applications,1996,(19):189-212.
[86]. J. Timmis, M. Neal, J. Hunt. An artificial immune system for data analysis[J].Biosystems,2000,55:143-150.
[87]. J. Timmis, M. Neal. A resource limited artificial immune system for data analysis[J].Knowledge-Based Systems,2001,14:121-130.
[88]. Nick D. L. Owens, Jon Timmis, Andrew Greensted and Andy Tyrrell. Elucidation of Tcell signaling models[J]. Journal of Theoretical Biology,2010,262(3):452-470.
[89]. De Castro L. N., Von Zuben F. J. An Evolutionary Immune Network for DataClustering[C]. In Proceedings of the IEEE Brazilian Symposium on Neural Networks,2000:84-89.
[90]. A. Watkins, J. Timmis. Artificial Immune Recognition System(AIRS): Revisions andRefinements[C]. Proceedings of the AISB2004Sysposium on The Immune System andCognition, pp:18-26.
[91].栗茂林,杜海峰,庄健等.基于模块性准则函数的模糊免疫网络聚类算法及其在故障诊断中的应用[J].机械工程学报,2010,46(9):100-106.
[92].郑永煌,李人厚.一种改进的基于免疫网络模型(aiNet)的故障诊断算法[J].控制与决策,2010,25(6):847-851.
[93].李刚,庄健,侯洪宁等.迭代可调节网络聚类及其在故障诊断中的应用[J].仪器仪表学报,2010,31(3):582-587.
[94]. Forrest, S., A.S. Perelson, L. Allen and R. Cherukuri. Self-nonself discrimination in acomputer[J]. In Proceedings of IEEE Symposium on Computer Security and Privacy,Oakland, CA, May16-18,1994,5:202-212.
[95]. Hofmeyr S A, Forrest S. Immunity by Design: An Artificial Immune System[C], Proc, ofGECCO’99,1999:1289-1296.
[96]. Hofmeyr S A, Forrest S. Architecture for an Artificial Immune System[J]. EvolutionaryComputation,2000,7(1):45-68.
[97]. Forrest S, Hofmeyr S A, Somayaji A. Computer Immunology[J], Communications of theACM,40(10):88-96.
[98]. Zhou Ji, Dipankar Dasgupta. V-detecotr: An efficient negative selection algorithm with“probably adequate” detector coverage[J]. Information Sciences,2009, pp:1390-1406.
[99]. Zhou Ji, Dipankar Dasgupta. Augmented negative selection algorithm withvariable-coverage detectors[J]. Congress on Evolutionary Computation,2004. CEC2004,Portland, Oregon, pp:123-136.
[100]. Zhou Ji, Dipankar Dasgupta. Revisiting negative selection algorithms[J]. EvolutionaryComputation,2007,15:33-49.
[101]. Senhua Yu, Dipankar Dasgupta. Conserved self pattern recognition algorithm[J].Artificial Immune Systems, Lecture Notes in Computer Science,2008,5132:279-290.
[102]. Senhua Yu, Dipankar Dasgupta. Conserved self pattern recognition algorithm with noveldetection strategy applied to breast cancer diagnosis[J]. Journal of Artificial Evolutionand Applications-Special issue on artificial evolution methods in the biological andbiomedical sciences,2009, pp:1-12.
[103]. Senhua Yu, Dipankar Dasgupta. An effective network-based Intrusion Detection usingConserved Self Pattern Recognition Algorithm augmented with near-deterministicdetector generation[C]. IEEE Symposium on Computational Intelligence in CyberSecurity (CICS),2011, pp:17-24.
[104]. T. Stibor, P. Mohr, J. Timmis, C. Eckert, Is negative selection appropriate for anomalydetection?[C] In;2005conference on Genetic and evolutionary computation (GECCO2005), Washington, DC, USA,2005.
[105]. T. Stibor, J. Timmis, C. Eckert, Generalization regions in hamming negative selection[J],in: Intelligent Information Processing and Web Mining, Vol.35, Advances in SoftComputing, Springer, Berlin/Heidelberg,2006, pp:447-456.
[106]. T. Stibor, P. Mohr, J. Timmis, C. Eckert, On permutation masks in hamming negativeselection[C], in:5th International Conference on Artificial Immune Systems (ICARIS2006), Oeiras, Portugal,2006.
[107]. Shulin Liu, Jiazhong Zhang, Wengang Shi and Wenhu Huang. Negative-selectionAlgorithm Based Approach for Fault Diagnosis of Rotary Machinery[J]. In Proceedingsof the American Control Conference, Anchorage, AK May8-10,2002, pp:3955-3960.
[108].刘树林,黄文虎,夏松波等.基于免疫机理的往复压缩机气阀故障检测方法[J].机械工程学报,2004,40(7):156-160.
[109].刘树林,王日新等.基于反面选择的压缩机振动在线监测方法研究[J].压缩机技术,2001,(5):9-10.
[110]. Shulin Liu, Yinghui Liu, Youfu Tang and Ruihong Jiang. A novel pattern recognitionbased on immunology[J], Information Technology Journal,2012,11(1):134-140.
[111]. X.Z. Gao, S.J. Ovaska, X. Wang, Genetic algorithms-based detector generation innegative selection algorithm[C], in:2006IEEE Mountain Workshop on Adaptive andLearning Systems,2006.
[112]. X.Z. Gao, S.J. Ovaska, X. Wang, M.-Y. Chow, Clonal Optimization of NegativeSelection Algorithm with Applications in Motor Fault Detection[C], in: IEEEInternational Conference on Systems, Man and Cybernetics (SMC’06), Taipei,2006.
[113]. J.M. Shapiro, G.B. Lamont, G.L. Peterson, An evolutionary algorithm to generatehyper-ellipsoid detectors for negative selection[C], in:2005Conference on Genetic andEvolutionary Computation, Washington, DC, USA,2005.
[114]. M. Ostaszewshi, F. Seredynski, P. Bouvry, Immune anomaly detection enhanced withevolutionary paradigms[C], in:8th Annual Conference on Genetic and EvolutionaryComputation (GECCO2006), Seattle, Washington, USA,2006.
[115]. M. Os taszewshi, F.Seredynski, P.Bouvry, Coevolutionary-based mechanisms fornetwork anomaly detection[J]. Journal of Mathematical Modeling and Algorithms6(2007):411-431.
[116]. Kephart J. O. A. Biologically Inspired Immune System for Computers[M]. In (Eds.)R.A.Brooks&P.Maes. Artificial Life IV Proceedings of the Fourth InternationalWorkshop on the Synthesis and Simulation of Living Systems. MIT Press,1994:130-139.
[117]. W. Luo, J. Wang, X. Wang, Evolutionary negative selection algorithms for anomalydetection[C]. in:8th Joint Conference on Information Science (JCIS2005), Salt LakeCity, UT, USA,2005.
[118]. Y. Zhang, W. Luo, Z. Zhang, B. Li, X. Wang, A hardware/software partitioning algorithmbased on artificial immune principles[J]. Applied Soft Computing2008,8:383-391.
[119]. W. Ma, D. Tran, D. Sharma, Negative selection with antigen feedback in intrusiondetection[C]. in:7th international conference on Artificial Immune Systems (ICARIS2008), Phuket, Thailand,2008.
[120]. A.J. Graaff, A.P. Engelbrecht, Optimized coverage of non-self with evolved lymphocytesin an artificial immune system[J]. International Journal of Computational IntelligenceResearch (IJCIR)2(2006):127-150.
[121]. S. Yu, D. Dasgupta, Conserved Self Pattern Recognition Algorithm[C]. in:7thInternational Conference on Artificial Immune Systems, Phuket, Thailand,2008.
[122]. U. Aickelin, J. Greensmith, Sensing Danger: Innate Immunology for IntrusionDetection[D]. The University of Nottingham, Nottingham, UK,2007.
[123]. Twycross J. Integrated innate and adaptive artificial immune systems applied to processanomaly detection[D]. School of Computer Science, University of Nottingham,2007.
[124]. W. Wang, S. Gao, Z. Tang, A complex artificial immune system[C]. in:2008FourthInternational Conference on Natural Computation,2008.
[125]. Zhou Ji, Dipankar Dasgupta. V-detector: An efficient negative selection algorithm with“probably adequate” detector coverage[J]. Information Sciences,2009:1390-1406.
[126]. Dipankar Dasgupta, Senhua Yu, Fernando Nino. Recent Advances in Artificial ImmuneSystems:Models and Applications[J]. Applied Soft Computing,2011,11:1574-1587.
[127].肖人彬,曹鹏彬,刘勇著.工程免疫计算[M].北京:科学出版社,2007.
[128]. Fabio Gonzalez， Dipankar Dasgupata and Luis Fernando Nino. A RandomizedReal-Valued Negative Selection Algorithm[J]. Artificial Immune Systems, Lecture Notesin Computer Science,2003,(2787):261-272.
[129]. Z. Ji, D. Dasgupta, Real-valued negative selection algorithm with variable-sizeddetectors[C]. Proceedings of GECCO, LNCS3102,2004, pp:287–298.
[130]. M. Ostaszewski, F. Seredynski, P. Bouvry. Immune anomaly detection enhanced withevolutionary paradigms[C].8th Annual Conference on Genetic and EvolutionaryComputation (GECCO2006),2006.
[131]. M. Ostaszewski, F. Seredynski, P. Bouvry. Coevolutionary-based mechanisms fornetwork anomaly detection[J]. Journal of Mathematical Modeling and Algorithms,2007(6):411–431.
[132]. J.M. Shapiro, G.B. Lamont, G.L. Peterson. An evolutionary algorithm to generatehyper-ellipsoid detectors for negative selection[C]. Conference on Genetic andEvolutionary Computation, Washington:2005.
[133]. Sankalp Balachandran, Dipankar Dasgupta, Fernando Nino, Deon Garrett. A Frameworkfor Evolving Multi-Shaped Detectors in Negative Selection[C]. Proceedings of the2007IEEE Symposium on Foundations of Computational Intelligence (FOCI2007).
[134]. Balachandran S., Dasgupta D., Nino F.. A Framework for Evolving Multi-ShapedDetectors in Negative Selection[C]. IEEE Symposium on Foundations of ComputationalIntelligence,2007:401-408.
[135]. Hamaker J S, Boggess L. Non-euclidean distance measures in AIRS, an artificial immuneclassification system[C]. Proceedings of2004Congress on Evolutionary Computation,2004:1067-1073.
[136].刘树林,黄文虎,夏松波等.基于免疫机理的往复压缩机气阀故障检测方法[J].机械工程学报,2004,40(7):156-160.
[137]. Hart E, Timmis J. Application areas of AIS: The past, the present and the future[J].Applied Soft Computing,2008,8(1):191-201.
[138].梁勤欧.人工免疫系统与GIS空间分析应用[M].武汉大学出版社,2011, pp:71-73.
[139]. Radoslav Tomovic et al. Vibration response of rigid rotor in unloaded rolling elementbearing[J]. International Journal of Mechanical Sciences.2010,52(9):1176-1185.
[140]. Tax D, Duin R. Support vector domain description[J]. Pattern Recognition Letters,1999,20(11–13):1191–1199.
[141]. Tax D, Support vector data description[J]. Machine Learning,2004,54:45-66.
[142]. Zheng, H.B., Chen, X.Z., Li, Z.Y., Zhu Z.K., He, S.D. Implementation and application ofa neural network fault diagnosis system based on wavelet transform[C]. Transactions ofthe Chinese Society of Agricultrual Machinery. Jiangsu university press,01.2002, pp:73-76.
[143]. Pan, Y.N., Chen, J., Guo, L. Robust bearing performance degradation assessment methodbased on improved wavelet packet-support vector data description[J]. MechanicalSystems and Signal Processing.2009,(6):669-681.
[144]. Lehrasab, N., Fararooy, S.: Formal definition of single throw mechanical equipment forfault di-agnosis[J]. Electronics Letters,1998,34(23):2231-2232.
[145].刘占生,窦唯,王晓伟等.基于人工免疫的直升机传动系统在线异常监测方法[J].航空学报,2007,28(3):739-744.
[146]. The Case Western Reserve University Bearing Data Center Website[EB/OL].http://www.eecs.cwru.edu/laboratory/bearing/.
[147].焦李成,杜海峰,刘芳等.免疫优化——计算、学习与识别[M].北京:科学出版社,2006.
[148]. Srinivasa, K.G., M. Jagadish, K.R. Venugopal and L.M. Patnaik. Data miningbased query processing using rough sets and genetic algorithms[C]. Proceedingsof the2007IEEE Symposium on Computational Intelligence and Data Mining,March1-April5, Honolulu, HI., pp:275-282.
[149]. Chang, X. and J.H. Lilly. Evolutionary design of a fuzzy classifier from data[J].IEEE Trans. Syst. Man Cybernetics B Cybernetics,2004,34:1894-1906.
[2].百度文库.往复式压缩机培训课件.http://wenku.baidu.com/view/96ff47afd1f34693daef3e12.html,2009.
[3].央视网.湖北长阳一家化工厂因机器故障引爆.http://news.cntv.cn/china/20120623/102644.shtml,2012.
[4].高金吉.机器故障诊治与自愈化[M].北京:高等教育出版社,2012.
[5].陈予恕.机械故障诊断的非线性动力学原理[J].机械工程学报,2007,43(1):25-34.
[6].韩清凯,于晓光.基于振动分析的现代机械故障诊断原理及应用[M].北京:科学出版社,2010.
[7]. P.M.Frank. Analytical and Qualitative Model Based Fault Diagnosis-A Survey and SomeNew Results[J]. European J.Control,2001,5(2):6-8.
[8].闻新,张洪钱,周露.控制系统的故障诊断和容错控制[M].北京:机械工业出版社,1998.
[9].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.
[10]. R.J.Patton, J.Chen, H.Benkhedda. A study on neuro-fuzzy systems for fault diagnosis[J].International Journal of Systems Science,2000,31(11):1441-1448.
[11]. Rolf Isermann. Process Fault Detection Based on Modeling and Estimation Methods-ASurvey[J]. Automatica,1984,20(4):387-404.
[12].于百胜,黄文虎.基于强跟踪滤波器的在线故障诊断方法[J].振动、测试与诊断,2003,23(4):260-263.
[13].姚红良,李鹤,李小彭等.裂纹碰摩耦合故障转子系统诊断分析[J].振动工程学报,2006,19(3):307-311.
[14].万方义,许庆余,宋笔锋等.非线性裂纹转子系统碰摩故障参变特性研究[J].机械强度,2006,28(5):633-638.
[15].林言丽,褚福磊.裂纹转子的刚度模型[J].机械工程学报,2008,44(1):114-120.
[16].孙保苍.动态油膜-裂纹转子系统非线性行为研究[J].农业机械学报,2004,35(5):170-173.
[17].郭保良,段志善,郑建校等.振动机械滚动轴承单点点蚀故障诊断研究[J].振动工程学报,2012,25(5):610-618.
[18].马辉,孙伟,任朝晖等.多盘悬臂转子系统制作松动故障研究[J].航空动力学报,2009,24(7):1512-1517.
[19].从云飞,陈进,董光明.基于滚动轴承故障诊断的AR预测滤波器阶数问题研究[J].振动与冲击,2012,31(4):44-47.
[20].李志农,何永勇,吴昭同等. ARMA模型盲辨识仿真研究及其在机械故障诊断中的应用[J].振动与冲击,2006,25(1):122-125.
[21].李志农,曾明如,韩捷等.主分量分析和因子隐Markov模型在机械故障诊断中的应用[J].机械强度,2007,29(1):25-29.
[22].李志农,郝伟,韩捷等.基于非线性时序模型盲辨识的因子隐Markov模型识别方法[J].机械工程学报,2007,43(1):191-195.
[23].于德介,陈淼峰,程军圣等.基于AR模型和支持向量机的转子系统故障诊断方法[J].系统工程理论与实践,2007,(5):152-156.
[24].马晋,江志农,高金吉.基于活塞杆轴心位置轨迹的往复压缩机故障诊断技术研究[J].振动工程学报,2012,25(4):453-459.
[25].褚福磊,彭志科,冯志鹏等.机械故障诊断中现代信号处理方法[M].北京:科学出版社,2009.
[26].王小玲,陈进,从云飞.基于时频的频带熵方法在滚动轴承故障识别中的应用[J].振动与冲击,2012,31(18):29-33.
[27].明阳,陈进.基于谱相关密度切片分析和SVM的滚动轴承故障诊断[J].振动与冲击,2010,29(1):196-199.
[28].董广明,陈进.基于循环频率能量的滚动轴承损伤程度识别[J].振动工程学报,2010,23(3):249-253.
[29].张进,冯志鹏,褚福磊.滚动轴承故障特征的时间-小波能量谱提取方法[J].机械工程学报,2011,47(17):44-49.
[30].明安波,褚福磊,张炜.滚动轴承故障特征提取的频谱自相关方法[J].机械工程学报,2012,48(19):65-70.
[31].任达千,杨世锡,吴昭同等. LMD时频分析方法的端点效应在旋转机械故障诊断中的影响[J].中国机械工程,2012,23(8):951-956.
[32].任达千,杨世锡,吴昭同等.信号瞬时频率直接计算法与Hilbert变换、Teager能量法比较[J].机械工程学报,2013,49(2).
[33].李凌均,韩捷,李朋勇等.矢双谱分析及其在机械故障诊断中的应用[J].机械工程学报,2011,47(17):50-54.
[34].王晓冬,何正嘉,訾艳阳.多小波自适应构造方法及滚动轴承复合故障诊断研究[J].振动工程学报,2010,23(4):438-443.
[35].王衍学,何正嘉,訾艳阳等.基于LMD的时频分析方法及其机械故障诊断应用研究[J].振动与冲击,2012,31(9):9-12.
[36].陈雪峰,向家伟,董洪波等.基于区间B样条小波有限元的转子裂纹定量识别[J].机械工程学报,2007,43(3):123-127.
[37].李常有,徐敏强,郭耸.基于改进的Hilbert-Huang变换的滚动轴承故障诊断[J].振动与冲击,2007,26(4):39-41.
[38].高清清,贾民平.基于EEMD的奇异谱熵在旋转机械故障诊断中的应用[J].东南大学学报(自然科学版),2011,41(5):998-1001.
[39].陈亚农,郜普刚,何田等.局部均值分解在滚动轴承故障综合诊断中的应用[J].振动与冲击,2012,31(3):73-78.
[40].毛永芳,秦树人,秦毅.重分配谱图和多窗谱在机械故障诊断中的应用[J].振动与冲击,2009,28(1):161-164.
[41].秦毅,秦树人,毛永芳.基于小波脊线的解调方法及其在旋转机械故障诊断中的应用[J].机械工程学报,2009,45(2):231-237.
[42].郝研,王太勇,万剑等.基于经验模式分解和广义维数的机械故障诊断[J].机械工程学报,2012,42(2):392-396.
[43].王太勇,何慧龙,王国锋等.基于经验模式分解和最小二乘支持矢量机的滚动轴承故障诊断[J].机械工程学报,2007,43(4):88-92.
[44].沈国际,陶利民,温熙森等.基于Wigner分布的齿轮箱振动信号相位估计[J].机械工程学报,2004,40(9):185-189.
[45].曹冲锋,杨世锡,杨将新等.基于双谱特征量的滚动轴承损伤部位识别[J].农业机械学报,2007,38(9):179-182.
[46].来五星,轩建平,史铁林等. Wigner-Ville时频分布研究及其在齿轮故障诊断中的应用[J].振动工程学报,2003,16(2):247-250.
[47].于德介,程军圣,杨宇. Hilbert-Huang变换在齿轮故障诊断中的应用[J].湖南大学学报（自然科学版）,2005,41(6):102-106.
[48].罗洁思,于德介,彭富强.基于EMD的多尺度形态学解调方法及其在机械故障诊断中的应用[J].振动与冲击,2009,28(11):84-86.
[49].程军圣,郑近德,杨宇.基于局部特征尺度分解的经验包络解调方法及其在机械故障诊断中的应用[J].机械工程学报,2012,48(19):87-94.
[50].吴军强,梁军.基于图论的故障诊断技术及其发展[J].机电工程,2003,20(5):188-190.
[51].闻竞竞,黄道.故障诊断方法综述[J].计算机技术与应用展望,2007:1306-1310.
[52].徐东,徐永成,陈循等.基于EMD的灰色模型的疲劳剩余寿命预测方法研究[J].振动工程学报,2011,24(1):104-110.
[53]. Mariela Cerrada, Juan Cardillo, Jose Aguilar and Raúl Faneite. Agents-based design forfault management systems in industrial processes[J]. Computers in Industry.2007,58(4):313-328.
[54]. Gang Niu, Tian Han, Bo-Suk Yang and Andy Chit Chiow Tan. Multi-agent decisionfusion for motor fault diagnosis[J]. Mechanical Systems and Signal Processing.2007,21(3):1285-1299.
[55].宋其江,徐敏强,王日新.基于MAS的航天器故障诊断系统模型研究[J].吉林大学学报工学版,2009,39(2):546-550.
[56]. Zhang S, Mathew J, Ma L, et al. Best basis-based intelligent machine fault diagnosis[J].Mechanical Systems and Signal Processing,2005,19(2):357-370.
[57]. Sampath S, Singh R. An integrated fault diagnostics model using genetic algorithm andneural networks[J]. Transactions of the ASME Journal of Engineering for Gas Turbinesand Power,2006,128(1):49-56.
[58]. Li RQ, Chen J, Wu X, et al. Fault diagnosis of rotating machinery based on SVD, FCMand RST[J]. International Journal of Advanced Manufacturing Technology,2005,27(1-2):128-135.
[59].雷亚国,何正嘉,訾艳阳.基于混合智能新模型的故障诊断[J].机械工程学报,2008,44(7):112-117.
[60]. L.N.D. Castro, F.J.V. Zuben, The clonal selection algorithm with engineeringapplications[C], Genetic and Evolutionary Computation Conference (GECCO’00)–Workshop Proceedings, Las Vegas, Nevada, USA,2000.
[61]. De Castro L. N., Von Zuben F. J. Immune and Neural Network Models: Theoretial andEmpirical Comparisons[J]. International Journal of Computational Intelligence andApplications (IJCIA),2001,1(3):239-257.
[62]. Thiago A. S. Masutti, L. N. De Castro. A Self-organizing Neural Network Using Ideasfrom the Immune System to Solve the Traveling Salesman Problem[J]. InformationSciences,2009,179(10):1454-1468.
[63]. Thiago A. S. Masutti, L. N. De Castro. Neuro-immune Approach to Solve RoutingProblems[J]. Neurocomputing,2009,72(10-12):2189-2197.
[64]. A. Ciccazzo, P. Conca, G.. Nicosia, G. Stracquadanio, An advanced clonal selectionalgorithm with ad hoc network-based hypermutation operators for synthesis of topologyand sizing of analog electrical circuits[C], in:7th International Conference on ArtificialImmune Systems, Phuket, Thailand,2008.
[65]. R. Halavati, S.B. Shouraki, M.J. Heravi, B.J. Jashmi, An artificial immune system withpartially specified antibodies[C], in:9th annual conference on Genetic and evolutionarycomputation (GECCO2007), London, England,2007.
[66]. P. May, J. Timmis, K. Mander, Immune and evolutionary approaches to softwaremutation testing[C], in:6th International Conference on Artificial Immune Systems(ICARIS2007), Santos, Brazil,2007.
[67].张向荣,焦李成.基于免疫克隆选择算法的特征选择[J].复旦学报,2004,43(5):926-929.
[68].戚玉涛,刘芳,焦李成.基于分布式人工免疫算法的数值优化[J].电子学报,2009,37(7):1554-1561.
[69].尚荣华,焦李成,马文萍.免疫克隆多目标优化算法求解约束优化问题[J].软件学报,2008, pp:2943-2956.
[70].杨咚咚,焦李成,公茂果等.求解偏好多目标优化的克隆选择算法[J].软件学报,2010,21(1):14-33.
[71].戚玉涛,刘芳,焦李成.求解大规模TSP问题的自适应归约免疫算法[J].软件学报,2008,19(6):1265-1273.
[72].戚玉涛,刘芳,焦李成.求解TSP问题免疫算法的动态疫苗策略[J].西安电子科技大学学报,2008,35(1):37-42.
[73].戚玉涛,焦李成,刘芳.基于并行人工免疫算法的大规模TSP问题求解[J].电子学报,2008,36(8):1552-1558.
[74].黄席樾,张著洪,何传江等.现代智能算法理论及应用[M].北京:科学出版社,2005.
[75].戴健,杨宏晖,杜方键等.一种用于风机故障诊断的免疫克隆特征选择算法[J].声学技术,2012,31(6):593-596.
[76].薛晋东,王琦.一种基于克隆选择算法的变压器故障诊断方法[J].电力学报,2012,27(2):123-126.
[77].侯胜利,王威,乔丽等.基于克隆聚类的特征提取与多传感器故障诊断[J].电光与控制,2010,17(6):69-72.
[78]. Jon Timmis, Mark Neal, John Hunt. An artificial immune system for data analysis[J],BioSystems,2000,55:143-150.
[79]. J. Kim, P.J. Bentley, U. Aickelin and Julie Greensmith et al. Immune system approachesto intrusion detection-a review[J], Natural Computing,2007,6:413-466.
[80]. Perelson A. Immune network theory[J]. Immunological Review,1989,110:5-36.
[81]. Ishiguro A, Shirai Y, Kondo T,et al. Immunoid: an architecture for behavior arbitrationbased on the immune networks//Proc. of IEEE/RSJ International Conference onIntelligent Robots and Systems, Osaka, Japan,1996:1730-1738.
[82]. Yanfei Zhong, Liangpei Zhang, Jianya Gong and Pingxiang Li. A Supervised ArtificialImmune Classifier for Remote-Sensing Imagery[J]. IEEE transactions on geoscience andremote sensing,2007,45(12):3957-3966.
[83]. De Castro L N, Von Zuben F J. Artificial immune system: Part I:basic theory andapplication. School of Electrical and Computer Engineering, State University ofCampinas, Campinas-SR, Brazil: Technical Report RT-DCA01,1999.
[84]. Ishida Y, Adachi N. Active Noise Control by an Immune Algorithm: Adaptation ImmuneSystem as an Evolution[C]. In Proc.1996IEEE Int Conf on Evolutionary Compuation,Nagoya,1996:150-153.
[85]. Hunt J. E., Cooke D. E. Learning Using An Artificial Immune System[J]. Journal ofNetwork and Computer Applications,1996,(19):189-212.
[86]. J. Timmis, M. Neal, J. Hunt. An artificial immune system for data analysis[J].Biosystems,2000,55:143-150.
[87]. J. Timmis, M. Neal. A resource limited artificial immune system for data analysis[J].Knowledge-Based Systems,2001,14:121-130.
[88]. Nick D. L. Owens, Jon Timmis, Andrew Greensted and Andy Tyrrell. Elucidation of Tcell signaling models[J]. Journal of Theoretical Biology,2010,262(3):452-470.
[89]. De Castro L. N., Von Zuben F. J. An Evolutionary Immune Network for DataClustering[C]. In Proceedings of the IEEE Brazilian Symposium on Neural Networks,2000:84-89.
[90]. A. Watkins, J. Timmis. Artificial Immune Recognition System(AIRS): Revisions andRefinements[C]. Proceedings of the AISB2004Sysposium on The Immune System andCognition, pp:18-26.
[91].栗茂林,杜海峰,庄健等.基于模块性准则函数的模糊免疫网络聚类算法及其在故障诊断中的应用[J].机械工程学报,2010,46(9):100-106.
[92].郑永煌,李人厚.一种改进的基于免疫网络模型(aiNet)的故障诊断算法[J].控制与决策,2010,25(6):847-851.
[93].李刚,庄健,侯洪宁等.迭代可调节网络聚类及其在故障诊断中的应用[J].仪器仪表学报,2010,31(3):582-587.
[94]. Forrest, S., A.S. Perelson, L. Allen and R. Cherukuri. Self-nonself discrimination in acomputer[J]. In Proceedings of IEEE Symposium on Computer Security and Privacy,Oakland, CA, May16-18,1994,5:202-212.
[95]. Hofmeyr S A, Forrest S. Immunity by Design: An Artificial Immune System[C], Proc, ofGECCO’99,1999:1289-1296.
[96]. Hofmeyr S A, Forrest S. Architecture for an Artificial Immune System[J]. EvolutionaryComputation,2000,7(1):45-68.
[97]. Forrest S, Hofmeyr S A, Somayaji A. Computer Immunology[J], Communications of theACM,40(10):88-96.
[98]. Zhou Ji, Dipankar Dasgupta. V-detecotr: An efficient negative selection algorithm with“probably adequate” detector coverage[J]. Information Sciences,2009, pp:1390-1406.
[99]. Zhou Ji, Dipankar Dasgupta. Augmented negative selection algorithm withvariable-coverage detectors[J]. Congress on Evolutionary Computation,2004. CEC2004,Portland, Oregon, pp:123-136.
[100]. Zhou Ji, Dipankar Dasgupta. Revisiting negative selection algorithms[J]. EvolutionaryComputation,2007,15:33-49.
[101]. Senhua Yu, Dipankar Dasgupta. Conserved self pattern recognition algorithm[J].Artificial Immune Systems, Lecture Notes in Computer Science,2008,5132:279-290.
[102]. Senhua Yu, Dipankar Dasgupta. Conserved self pattern recognition algorithm with noveldetection strategy applied to breast cancer diagnosis[J]. Journal of Artificial Evolutionand Applications-Special issue on artificial evolution methods in the biological andbiomedical sciences,2009, pp:1-12.
[103]. Senhua Yu, Dipankar Dasgupta. An effective network-based Intrusion Detection usingConserved Self Pattern Recognition Algorithm augmented with near-deterministicdetector generation[C]. IEEE Symposium on Computational Intelligence in CyberSecurity (CICS),2011, pp:17-24.
[104]. T. Stibor, P. Mohr, J. Timmis, C. Eckert, Is negative selection appropriate for anomalydetection?[C] In;2005conference on Genetic and evolutionary computation (GECCO2005), Washington, DC, USA,2005.
[105]. T. Stibor, J. Timmis, C. Eckert, Generalization regions in hamming negative selection[J],in: Intelligent Information Processing and Web Mining, Vol.35, Advances in SoftComputing, Springer, Berlin/Heidelberg,2006, pp:447-456.
[106]. T. Stibor, P. Mohr, J. Timmis, C. Eckert, On permutation masks in hamming negativeselection[C], in:5th International Conference on Artificial Immune Systems (ICARIS2006), Oeiras, Portugal,2006.
[107]. Shulin Liu, Jiazhong Zhang, Wengang Shi and Wenhu Huang. Negative-selectionAlgorithm Based Approach for Fault Diagnosis of Rotary Machinery[J]. In Proceedingsof the American Control Conference, Anchorage, AK May8-10,2002, pp:3955-3960.
[108].刘树林,黄文虎,夏松波等.基于免疫机理的往复压缩机气阀故障检测方法[J].机械工程学报,2004,40(7):156-160.
[109].刘树林,王日新等.基于反面选择的压缩机振动在线监测方法研究[J].压缩机技术,2001,(5):9-10.
[110]. Shulin Liu, Yinghui Liu, Youfu Tang and Ruihong Jiang. A novel pattern recognitionbased on immunology[J], Information Technology Journal,2012,11(1):134-140.
[111]. X.Z. Gao, S.J. Ovaska, X. Wang, Genetic algorithms-based detector generation innegative selection algorithm[C], in:2006IEEE Mountain Workshop on Adaptive andLearning Systems,2006.
[112]. X.Z. Gao, S.J. Ovaska, X. Wang, M.-Y. Chow, Clonal Optimization of NegativeSelection Algorithm with Applications in Motor Fault Detection[C], in: IEEEInternational Conference on Systems, Man and Cybernetics (SMC’06), Taipei,2006.
[113]. J.M. Shapiro, G.B. Lamont, G.L. Peterson, An evolutionary algorithm to generatehyper-ellipsoid detectors for negative selection[C], in:2005Conference on Genetic andEvolutionary Computation, Washington, DC, USA,2005.
[114]. M. Ostaszewshi, F. Seredynski, P. Bouvry, Immune anomaly detection enhanced withevolutionary paradigms[C], in:8th Annual Conference on Genetic and EvolutionaryComputation (GECCO2006), Seattle, Washington, USA,2006.
[115]. M. Os taszewshi, F.Seredynski, P.Bouvry, Coevolutionary-based mechanisms fornetwork anomaly detection[J]. Journal of Mathematical Modeling and Algorithms6(2007):411-431.
[116]. Kephart J. O. A. Biologically Inspired Immune System for Computers[M]. In (Eds.)R.A.Brooks&P.Maes. Artificial Life IV Proceedings of the Fourth InternationalWorkshop on the Synthesis and Simulation of Living Systems. MIT Press,1994:130-139.
[117]. W. Luo, J. Wang, X. Wang, Evolutionary negative selection algorithms for anomalydetection[C]. in:8th Joint Conference on Information Science (JCIS2005), Salt LakeCity, UT, USA,2005.
[118]. Y. Zhang, W. Luo, Z. Zhang, B. Li, X. Wang, A hardware/software partitioning algorithmbased on artificial immune principles[J]. Applied Soft Computing2008,8:383-391.
[119]. W. Ma, D. Tran, D. Sharma, Negative selection with antigen feedback in intrusiondetection[C]. in:7th international conference on Artificial Immune Systems (ICARIS2008), Phuket, Thailand,2008.
[120]. A.J. Graaff, A.P. Engelbrecht, Optimized coverage of non-self with evolved lymphocytesin an artificial immune system[J]. International Journal of Computational IntelligenceResearch (IJCIR)2(2006):127-150.
[121]. S. Yu, D. Dasgupta, Conserved Self Pattern Recognition Algorithm[C]. in:7thInternational Conference on Artificial Immune Systems, Phuket, Thailand,2008.
[122]. U. Aickelin, J. Greensmith, Sensing Danger: Innate Immunology for IntrusionDetection[D]. The University of Nottingham, Nottingham, UK,2007.
[123]. Twycross J. Integrated innate and adaptive artificial immune systems applied to processanomaly detection[D]. School of Computer Science, University of Nottingham,2007.
[124]. W. Wang, S. Gao, Z. Tang, A complex artificial immune system[C]. in:2008FourthInternational Conference on Natural Computation,2008.
[125]. Zhou Ji, Dipankar Dasgupta. V-detector: An efficient negative selection algorithm with“probably adequate” detector coverage[J]. Information Sciences,2009:1390-1406.
[126]. Dipankar Dasgupta, Senhua Yu, Fernando Nino. Recent Advances in Artificial ImmuneSystems:Models and Applications[J]. Applied Soft Computing,2011,11:1574-1587.
[127].肖人彬,曹鹏彬,刘勇著.工程免疫计算[M].北京:科学出版社,2007.
[128]. Fabio Gonzalez， Dipankar Dasgupata and Luis Fernando Nino. A RandomizedReal-Valued Negative Selection Algorithm[J]. Artificial Immune Systems, Lecture Notesin Computer Science,2003,(2787):261-272.
[129]. Z. Ji, D. Dasgupta, Real-valued negative selection algorithm with variable-sizeddetectors[C]. Proceedings of GECCO, LNCS3102,2004, pp:287–298.
[130]. M. Ostaszewski, F. Seredynski, P. Bouvry. Immune anomaly detection enhanced withevolutionary paradigms[C].8th Annual Conference on Genetic and EvolutionaryComputation (GECCO2006),2006.
[131]. M. Ostaszewski, F. Seredynski, P. Bouvry. Coevolutionary-based mechanisms fornetwork anomaly detection[J]. Journal of Mathematical Modeling and Algorithms,2007(6):411–431.
[132]. J.M. Shapiro, G.B. Lamont, G.L. Peterson. An evolutionary algorithm to generatehyper-ellipsoid detectors for negative selection[C]. Conference on Genetic andEvolutionary Computation, Washington:2005.
[133]. Sankalp Balachandran, Dipankar Dasgupta, Fernando Nino, Deon Garrett. A Frameworkfor Evolving Multi-Shaped Detectors in Negative Selection[C]. Proceedings of the2007IEEE Symposium on Foundations of Computational Intelligence (FOCI2007).
[134]. Balachandran S., Dasgupta D., Nino F.. A Framework for Evolving Multi-ShapedDetectors in Negative Selection[C]. IEEE Symposium on Foundations of ComputationalIntelligence,2007:401-408.
[135]. Hamaker J S, Boggess L. Non-euclidean distance measures in AIRS, an artificial immuneclassification system[C]. Proceedings of2004Congress on Evolutionary Computation,2004:1067-1073.
[136].刘树林,黄文虎,夏松波等.基于免疫机理的往复压缩机气阀故障检测方法[J].机械工程学报,2004,40(7):156-160.
[137]. Hart E, Timmis J. Application areas of AIS: The past, the present and the future[J].Applied Soft Computing,2008,8(1):191-201.
[138].梁勤欧.人工免疫系统与GIS空间分析应用[M].武汉大学出版社,2011, pp:71-73.
[139]. Radoslav Tomovic et al. Vibration response of rigid rotor in unloaded rolling elementbearing[J]. International Journal of Mechanical Sciences.2010,52(9):1176-1185.
[140]. Tax D, Duin R. Support vector domain description[J]. Pattern Recognition Letters,1999,20(11–13):1191–1199.
[141]. Tax D, Support vector data description[J]. Machine Learning,2004,54:45-66.
[142]. Zheng, H.B., Chen, X.Z., Li, Z.Y., Zhu Z.K., He, S.D. Implementation and application ofa neural network fault diagnosis system based on wavelet transform[C]. Transactions ofthe Chinese Society of Agricultrual Machinery. Jiangsu university press,01.2002, pp:73-76.
[143]. Pan, Y.N., Chen, J., Guo, L. Robust bearing performance degradation assessment methodbased on improved wavelet packet-support vector data description[J]. MechanicalSystems and Signal Processing.2009,(6):669-681.
[144]. Lehrasab, N., Fararooy, S.: Formal definition of single throw mechanical equipment forfault di-agnosis[J]. Electronics Letters,1998,34(23):2231-2232.
[145].刘占生,窦唯,王晓伟等.基于人工免疫的直升机传动系统在线异常监测方法[J].航空学报,2007,28(3):739-744.
[146]. The Case Western Reserve University Bearing Data Center Website[EB/OL].http://www.eecs.cwru.edu/laboratory/bearing/.
[147].焦李成,杜海峰,刘芳等.免疫优化——计算、学习与识别[M].北京:科学出版社,2006.
[148]. Srinivasa, K.G., M. Jagadish, K.R. Venugopal and L.M. Patnaik. Data miningbased query processing using rough sets and genetic algorithms[C]. Proceedingsof the2007IEEE Symposium on Computational Intelligence and Data Mining,March1-April5, Honolulu, HI., pp:275-282.
[149]. Chang, X. and J.H. Lilly. Evolutionary design of a fuzzy classifier from data[J].IEEE Trans. Syst. Man Cybernetics B Cybernetics,2004,34:1894-1906.