Wavelet based fault detection of aeronautic engine vanes by borescope
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摘要: 发动机孔探技术是航空发动机内部故障检测的重要技术,而叶片损伤则是发动机内部的一种常见损伤.针对发动机叶片损伤的特点,通过对损伤模型的分析,应用SUSAN(Smallest Univalue Segment Assimilating Nucleus)边缘检测算法对叶片损伤进行定位,应用小波变换对孔探图像进行分解,得到不同位置处的不同频率分量.并根据损伤模型的分析通过已定位的损伤位置来确定损伤法线方向的频谱分量,计算出损伤处除直流分量之外的其它频率分量的频谱能量,从而依据损伤频谱能量检测并估计发动机叶片的损伤.实验结果表明,应用此方法可以定量检测并估计航空发动机叶片表面损伤情况,为检测并估计发动机叶片损伤提供了理论和实践依据.Abstract: Using borescope equipment to inspect the inside of turbofan engines is an important technology to the daily fault diagnostic of aeronautic engine. In all the aeronautic engine interior faults, vane′s face fault is a regular one. Towards vane′s face fault, a kind of fault detection and estimation method was presented. First, vane′s face fault was detected by the SUSAN(smallest univalue segment assimilating nucleus) edge detector. Second, the image was analyzed by wavelet transforms to achieve the different frequency components at different locations. Third, the energy of the alternating components around fault was computed by the spectrum. And at last the vane′s face fault was detected and estimated by the energy. Test result shows that this method can quantificationally detect the fault of aeronautic engine vanes and estimate the grade of the fault. It provides a scientific basis for the fault detection and estimation of aeronautic engine vanes by borescope.
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Key words:
- turbofan engines /
- wavelet transforms /
- edge detection /
- spectrum analysis /
- energy conservation
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