CAJ | 학술논문

静电监测技术以其优越的早期故障发现能力为航空发动机主轴轴承的状态监测提供了一种有效手段，但是单一特征参数对轴承全寿命状态反应存在不一致或不灵敏，针对这一问题，提出一种基于支持向量描述(SVDD)的静电监测多特征参数融合评估方法。首先通过主元分析将高维数据进行简化处理，实现数据从高维到低维的转化，然后建立正常状态的支持向量描述基准模型，以测试数据与基准模型的距离作为反映轴承性能退化的量化指标，并与振动监测进行了对比。实验表明，所提方法能够更有效反映轴承性能退化状态的变化。
정전감측기술이기우월적조기고장발현능력위항공발동궤주축축승적상태감측제공료일충유효수단，단시단일특정삼수대축승전수명상태반응존재불일치혹불령민，침대저일문제，제출일충기우지지향량묘술(SVDD)적정전감측다특정삼수융합평고방법。수선통과주원분석장고유수거진행간화처리，실현수거종고유도저유적전화，연후건립정상상태적지지향량묘술기준모형，이측시수거여기준모형적거리작위반영축승성능퇴화적양화지표，병여진동감측진행료대비。실험표명，소제방법능구경유효반영축승성능퇴화상태적변화。
Electrostatic monitoring technology for its superior ability to monitor early failure of the main shaft bearing of aircraft engine condition monitoring provides an effective means,but a single char-acteristic parameters for the whole life of bearings status is inconsistency or insensitive.To solve this problem,we propose an electrostatic monitoring multi-characteristic parameters fusion evaluation method based on SVDD.Firstly,we simplify high-dimensional data by using PCA,transform data from high-di-mensional to low-dimensional,and then create a normal SVDD model.We calculate the distance between the test data and the reference model as quantitative indicators of bearing performance degradation,and compare it with the vibration monitoring data.Experiments show that the method can reflect the changes more effectively in the state of performance degradation.