基于免疫理论的航空发动机磨损故障智能诊断
收稿日期: 2014-06-11
修回日期: 2014-10-10
网络出版日期: 2014-12-30
Intelligent diagnosis for aircraft engine wear fault based on immune theory
Received date: 2014-06-11
Revised date: 2014-10-10
Online published: 2014-12-30
针对航空发动机磨损故障诊断技术智能化、精确化的发展要求,以传统油液监测技术为基础,结合人工免疫系统(AIS)具有的自适应特性、学习记忆特性及识别特性等优点,提出了一种航空发动机磨损故障的智能诊断方法。该方法首先利用人工免疫理论的反面选择原理生成检测器,优化后的检测器生成算法提高了初始检测器的代表性及覆盖性;然后利用故障样本训练出成熟的检测器,使航空发动机典型的磨损状态信息存储在检测器中,实现对故障模式的有效学习和记忆;最后通过检测器的激活发现航空发动机的磨损故障。对油样数据的实例分析结果表明,该方法对航空发动机磨损故障具有较强的识别能力,对磨损状态有很好的监测效果。
马安祥 , 李艳军 , 曹愈远 , 汪震宇 , 安罡 . 基于免疫理论的航空发动机磨损故障智能诊断[J]. 航空学报, 2015 , 36(6) : 1896 -1904 . DOI: 10.7527/S1000-6893.2014.0357
To meet the development requirements of intelligentization and accuracy for the aircraft engine wear fault diagnosis technology, an intelligent diagnosis method for aircraft engine wear fault is proposed based on the traditional oil monitoring technology, and this method is combined with the artificial immune system's (AIS) advantages, such as adaptive characteristic, learning and memory characteristic and recognition characteristics. Firstly, the method uses negative selection principle of artificial immune theory to build detectors. The optimized immune algorithm improves the initial detectors' representativeness and coverage. And then fault samples are used to train and generate mature detectors. So the typical information of aircraft engine wear fault is stored in the mature detectors, implementing effective learning and memory of the failure modes. Finally, wear fault of the aircraft engine can be found through the activated detectors. The case analytical results of the sample data demonstrate that the method has strong ability to recognize aircraft engine wear faults and it has very good monitoring effect to wear condition.
Key words： aircraft engine; fault diagnosis; immune system; oil analysis; wear
[1] Li Y J, Zuo H F, Wu Z F. Intelligent diagnostics for engine wear failure based on debris analysis[J]. Journal of Nanjing University of Aeronautics and Astronautics, 2001, 33(3): 221-226 (in Chinese). 李艳军, 左洪福, 吴振峰. 基于磨粒分析方法的发动机磨损故障智能诊断技术[J]. 南京航空航天大学学报, 2001, 33(3): 221-226.
[2] Williams J H, Davies A, Drake P R. Condition-based maintenance and machine diagnostics[J]. Chapman and Hall, 1992, 87(5): 74-86.
[3] Wu Z F. The research of engine wear faults diagnosis based on debris analysis and data fusion[D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2001 (in Chinese). 吴振锋. 基于磨粒分析和信息融合的发动机磨损故障诊断技术研究[D]. 南京: 南京航空航天大学, 2001.
[4] Chen G, Zuo H F. Expert systems of engine wear fault diagnosis based on knowledge rule[J]. Journal of Aerospace Power, 2004, 19(1): 23-29 (in Chinese). 陈果, 左洪福. 基于知识规则的发动机磨损故障诊断专家系统[J]. 航空动力学报, 2004, 19(1): 23-29.
[5] Wen Z H, Zuo H F. A diagnosis method for aero engine wear fault based on rough sets theory and integrated neural network[J]. China Mechanical Engineering, 2007, 18(21): 2580-2584 (in Chinese). 文振华, 左洪福. 基于粗糙集-集成神经网络的航空发动机磨损故障诊断方法[J]. 中国机械工程, 2007, 18(21): 2580-2584.
[6] Luh G C, Cheng W C. Immune model-based fault diagnosis[J]. Mathematics and Computers in Simulation, 2005, 67(6): 515-539.
[7] Mo H W. Principle and application of artificial immune system[M]. Harbin: Harbin Institute of Technology Press, 2003 (in Chinese). 莫宏伟. 人工免疫系统原理与应用[M]. 哈尔滨: 哈尔滨工业大学出版社, 2003.
[8] Liu Y H. The research of anomaly detection and fault diagnosis based on artificial immune system[D]. Shanghai: Shanghai University, 2013 (in Chinese). 刘颖慧. 基于人工免疫系统的异常状态监测及故障诊断研究[D]. 上海: 上海大学, 2013.
[9] Laurentys C A, Palhares R M, Caminhas W M. Design of an artificial immune system based on danger model for fault detection[J]. Expert Systems with Applications, 2010, 37(7): 5145-5152.
[10] Ghosh K, Srinivasan R. Immune-system-inspired approach to process monitoring and fault diagnosis[J]. Industrial and Engineering Chemistry Research, 2010, 50(3): 1637-1651.
[11] Aydin I, Karakose M, Akin E. An adaptive artificial immune system for fault classification[J]. Journal of Intelligent Manufacturing, 2012, 23(5): 1489-1499.
[12] Sha J G. The research on fault diagnosis of aero-engine digital control system based on artificial immune theory[D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2007 (in Chinese). 沙金刚. 基于免疫原理的发动机数控系统故障诊断研究[D]. 南京: 南京航空航天大学, 2007.
[13] Hou S L, Wang W, Qiao L, et al. A neural network model of compressor stall detection based on negative selection principle[J]. Electronics Optics and Control, 2010, 17(5): 38-41 (in Chinese). 侯胜利, 王维, 乔丽, 等. 压气机失速检测的神经网络反面选择模型[J]. 电光与控制, 2010, 17(5): 38-41.
[14] Gao J W, Zhang P L, Wu D H, et al. A fault diagnosis approach of engine using immunological theory and spectrometric oil analysis[J]. Chinese Internal Combustion Engine Engineering, 2008, 29(1): 77-80 (in Chinese). 高经纬, 张培林, 吴定海, 等. 一种基于油液光谱分析和免疫原理的内燃机磨损故障诊断方法[J]. 内燃机工程, 2008, 29(1): 77-80.
[15] Wu X. Research on aero-engine intelligent monitoring based on the oil analysis[D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2012 (in Chinese). 吴晓. 基于油液分析的航空发动机磨损状态智能监测研究[D]. 南京: 南京航空航天大学, 2012.
[16] Forrest S, Perelson A S, Allen L, et al. Self-nonself discrimination in a computer[C]//2012 IEEE Symposium on Security and Privacy. Piscataway, NJ: IEEE Press, 1994: 202.
[17] Dasgupta D, Forrest S. Artificial immune systems in industrial applications[C]//Proceedings of the Second International Conference on Intelligent Processing and Manufacturing of Materials, 1999, 1: 257-267.
[18] Roemer M J, Nwadiogbu E O, Bloor G. Development of diagnostic and prognostic technologies for aerospace health management applications[C]//IEEE Proceedings of Aerospace Conference. Piscataway, NJ: IEEE Press, 2001, 6: 3139-3147.
[19] Timmis J, Neal M, Hunt J. An artificial immune system for data analysis[J]. Biosystems, 2000, 55(1): 143-150.
[20] Xu X M, Wang R G, Hou S L. Intelligence fusion approach to fault detection based on negative selection principle and its application[J]. Systems Engineering and Electronics, 2009, 31(8): 2029-2032 (in Chinese). 徐学邈, 王如根, 侯胜利. 基于反面选择原理的智能融合故障检测模型及其应用[J]. 系统工程与电子技术, 2009, 31(8): 2029-2032.
/
| 〈 |
|
〉 |
CJA