北京航空航天大学学报 ›› 2016, Vol. 42 ›› Issue (1): 35-40.doi: 10.13700/j.bh.1001-5965.2015.0070

• 论文 • 上一篇    下一篇

基于贝叶斯网络的故障诊断系统性能评价

于劲松1,2, 沈琳1, 唐荻音1, 刘浩1,3   

  1. 1. 北京航空航天大学自动化科学与电气工程学院, 北京 100083;
    2. 先进航空发动机协同创新中心, 北京 100083;
    3. 解放军 95809部队93分队, 沧州 061736
  • 收稿日期:2015-01-31 出版日期:2016-01-20 发布日期:2016-01-28
  • 通讯作者: 于劲松,Tel.:010-82338693E-mail:jinsong_yu@126.com E-mail:jinsong_yu@126.com
  • 作者简介:于劲松男,副教授,硕士生导师。主要研究方向:预测与健康管理技术、自动测试系统。Tel.:010-82338693E-mail:jinsong_yu@126.com

Performance evaluation of fault diagnosis system based on Bayesian network

YU Jinsong1,2, SHEN Lin1, TANG Diyin1, LIU Hao1,3   

  1. 1. School of Automation and Electrical Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100083, China;
    2. Collaborative Innovation Center of Advanced Aero-Engine, Beijing 100083, China;
    3. Unit 93, Army 95809 of PLA, Cangzhou 061736, China
  • Received:2015-01-31 Online:2016-01-20 Published:2016-01-28

摘要: 故障诊断系统的性能评价是开发和验收故障诊断系统不可或缺的重要环节.针对基于贝叶斯网络(BN)故障诊断系统的性能评价需要,考虑系统诊断结果真实分布,提出采用二项分布参数估计方法来计算诊断准确度的置信区间,采用准确度期望值及其置信区间全面客观评价诊断模型的性能,形成贝叶斯网络模型诊断能力的量化评价指标,为诊断结果的可接受、可信程度以及诊断模型的训练充分性提供参考依据.最后通过燃油系统故障诊断实例验证所述性能评价的有效性.

关键词: 贝叶斯网络(BN), 诊断, 性能, 准确度, 置信区间

Abstract: Assessing whether a newly developed fault diagnosis system is effective is an important issue to ensure diagnosis system performance.Due to the requirement of evaluating the performance of the fault diagnosis system based on Bayesian network (BN), an evaluation method using a modified binomial distribution was developed, considering the real distribution of diagnosis results. The parameters of the modified binomial distribution were estimated using training data during the training process of fault diagnosis system, and both diagnosis accuracy and confidence interval of a diagnostic system could be calculated simultaneously by this evaluation method. The quantitive evaluation indices provided by the proposed evaluation method greatly contributed to the evaluation of acceptability and reliability of a Bayesian network-based diagnosis system, and were of great significance in supporting diagnosis system training. In conclusion, the effectiveness of the proposed evaluation method was validated by an example concerning a fault diagnosis system for the aircraft fuel system.

Key words: Bayesian network (BN), diagnosis, performance, accuracy, confidence interval

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