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Research on Aero-engine Bearing Fault Using Acoustic Emission Technique Based on Wavelet Packet Decomposition and Support Vector Machine

机译:基于小波包分解和支持向量机的声发射技术研究空气发动机轴承故障

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The normal running of aero-engine is an important assurance for the plane's flight safety. The development trend of condition monitoring and fault diagnostic technology of engine is to be real-time and intelligent. In this paper, a new method based on the acoustic emission technology is proposed for the fault diagnosis of aero-engine bearings. Using the analytical method of wavelet packet decomposition and support vector machine to identify the fault type of bearings. Firstly, the measurement signal is decomposed by using wavelet packet decomposition. And the energy value of different frequency bands was extracted as eigenvector, which is used as training sample of support vector machine. The analysis result of the experimental data proves that the method has high recognition rate. This essay provides a new approach to the research of fault diagnostic of aero-engine bearings, and it throws new light on the further research.
机译:航空发动机的正常运行是飞机飞行安全的重要保证。发动机状况监测和故障诊断技术的发展趋势是实时和智能化。本文提出了一种基于声发射技术的新方法,用于航空发动机轴承的故障诊断。使用小波包分解的分析方法和支持向量机识别轴承的故障类型。首先,通过使用小波分组分解来分解测量信号。不同频带的能量值被提取为特征向量,其用作支持向量机的训练样本。实验数据的分析结果证明了该方法具有高识别率。本文为航空发动机轴承故障诊断的研究提供了一种新方法,并在进一步的研究中投入了新的光线。

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