基于案例推理的鱼雷制导系统故障诊断方法

基金项目: 海军大连舰艇学院2008年重点资助项目(2008DLJY005)

Fault Diagnosis Method of Torpedo Guidance System Based on Case-based Reasoning

1.
Department of Equipment Automation Dalian Naval Academy, Dalian 116018, China
2.
91550th Unit, The People′s Liberation Army of China, Dalian 116023, China

摘要: 为了解决传统的基于规则推理(RBR)方法存在的不足, 将基于案例推理(CBR)方法引入鱼雷制导故障诊断系统中。阐述了案例推理的诊断策略, 重点描述了二级案例检索策略及其算法, 进而从案例描述、系统结构和工作流程3个方面介绍了基于案例推理的鱼雷制导系统故障诊断方法设计, 并在VB6.0平台上开发了故障诊断系统, 最后通过具体的故障案例比较了RBR和CBR两种方法。案例分析结果表明, 所开发的基于案例推理故障诊断系统能够提高鱼雷制导系统维修的准确性和高效性。
- 鱼雷 /
- 制导系统 /
- 故障诊断 /
- 案例推理
Abstract: To overcome the shortcomings of traditional rule-based reasoning(RBR), this study introduced case-based reasoning (CBR) into torpedo guidance fault diagnosis system. The diagnosis strategy of CBR was analyzed, and the two-level case retrieval mechanism and the algorithm were explained. The design of the torpedo guidance fault diagnosis system based on CBR was particularly described from three aspects, i.e., description of case, structure of diagnosis sys-tem, and process of working flow. And this system was developed with VB6.0. Fault case analysis shows that the pro-posed system can enhance the efficiency and correctness of fault diagnosis of torpedo guidance system.
- torpedo /
- guidance system /
- fault diagnosis /
- case-based reasoning

[1]	宋保维, 周明. 鱼雷系统工程原理与方法[M]. 哈尔滨: 哈尔滨工程大学出版社, 2010.
[2]	王伟, 孙起, 许军. 鱼雷自导系统BIT设计与分析[J]. 鱼雷技术, 2010, 18(1): 31-34. Wang Wei, Sun Qi, Xu Jun. Design and Analysis of Built-in Test for Torpedo Homing System[J]. Torpedo Technology, 2010, 18(1): 31-34.
[3]	王东, 刘怀亮, 徐国华. 案例推理在故障诊断中的应用[J]. 计算机工程, 2003, 29(12): 10-12. Wang Dong, Liu Huai-liang, Xu Guo-hua. Application of Case-based Reasoning in Faulty Diagnoses System[J]. Computer Engineering, 2003, 29(12): 10-12.
[4]	韩云东, 袁群哲, 尹伟伟, 等. 电罗经故障诊断的案例推理原理与方法研究[J]. 中国航海, 2009, 32 (6): 22-25. Han Yun-dong, Yuan Qun-zhe, Yin Wei-wei, et al. Theory and Method of Fault Diagnosis in Gyrocompass Based on CBR[J]. Navigation of China, 2009, 32(6): 22-25.
[5]	Ha M T. Distributed Case-Based Reasoning for Fault Management[J]. Lecture Notes in Computer Science, 2007, 45(3): 200-203.
[6]	Yang B S, Han T, Kim, et al. Integration of ART-Kohonen Neural Network and Case-based Reasoning for Intelligent Fault Diagnosis[J]. Expert Systems with Applications, 2004, 26(3): 387-395.