| 基于D-S证据理论的航空发动机磨损故障智能融合诊断方法 |
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| 引用本文： | 文振华,陈果.基于D-S证据理论的航空发动机磨损故障智能融合诊断方法[J].机械科学与技术(西安),2005,24(9):1018-1021. |
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| 作者姓名： | 文振华 陈果 |
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| 作者单位： | 南京航空航天大学民航学院,南京210016 |
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| 摘 要： | 油样分析方法目前已成为航空发动机磨损故障诊断的重要手段,但单一油样分析技术的诊断准确率均有限,为了提高故障诊断的精度,本文提出了基于D-S证据理论的发动机磨损故障智能融合诊断方法。首先用BP神经网络实现发动机磨损故障的单项智能诊断,然后,充分利用神经网络诊断结果,用D-S证据理论实现了磨损故障的融合诊断。最后,算例验证了本文方法的有效性。
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| 关 键 词： | 航空发动机 磨损 故障诊断 数据融合 神经网络 D-S证据理论 |
| 文章编号： | 1003-8728（2005）09-1018-04 |
| 收稿时间： | 2004-09-20 |
| 修稿时间： | 2004-09-20 |
An Intelligent Fusion Technique for Diagnosis of Engine Wear Fault Based on D-S Evidence Theory |
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| Wen ZheHua;Chen Guo.An Intelligent Fusion Technique for Diagnosis of Engine Wear Fault Based on D-S Evidence Theory[J].Mechanical Science and Technology,2005,24(9):1018-1021. |
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| Authors: | Wen ZheHua;Chen Guo |
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| Abstract: | Oil analysis technology has become a common technology in the filed of aero-engine wear fault diagnosis.The effectiveness of individual oil analysis technology,however,is limited in its accuracy.An intelligent fusion technique based on the Dempster-Shafer(D-S) evidence theory is proposed to improve the diagnosis accuracy.Firstly,the BP neural network is employed to carry out single aspect diagnosis;then the final conclusions are reached by combining the results of different diagnostic tools based on the Dempster-Shafer evidence theory.Examples show the validity of the technique proposed in this paper. |
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| Keywords: | Aero-engine Wear Fault diagnosis Data fusion Neural network D-S evidence theory |
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