一种航空发动机滚动轴承磨损故障监测技术

针对航空发动机滚动轴承磨损状态监测对长轴尺寸大于10μm的故障敏感磨粒检测的需要，研制了多功能油液磨粒智能检测与诊断系统，克服了传统光谱分析对大磨粒不敏感的缺点，以及传统铁谱分析检测步骤烦琐的不足，可直接对流动的油液中大于10μm的运动磨粒进行检测.提出了油液运动磨粒的7个数字特征参数及其识别策略，实现了磨粒的自动识别，识别率基本上达到99%以上.利用实际的航空发动机油样进行了试验验证，并与传统光谱分析进行了对比，试验结果表明该系统较光谱分析具有更强的检测力和更优的时效性. 

A fault monitoring technique for wear of aero-engine rolling bearing

Multiple intelligent debris classifying system (MIDCS) to detect sensitive wear particles whose size are 10μm or more in long axis on the aero-engine rolling bearing for wear fault monitoring was developed. The MIDCS overcomed the shortcomings, such as the insensitivity to large wear particles in the traditional spectral analysis, and cumbersome detection steps in the ferrographic analysis. The MIDCS could be used to directly detect wear particles whose size are 10μm or more in long axis in the flowing oil. Seven digital characteristic parameters and their identification were proposed for the moving wear particles in the flowing oil, implemented the auto-identification for the wear particles. The basic identification accuracy of the MIDCS is above 99%. Through the experiments on the real oil samples from aero-engines, result shows that the MIDCS is superior to the spectral analysis in terms of the ability and timeliness on detecting the wear abrasion fault of the aero-engine rolling bearing.