CAJ | 학술논문

状态维修根据设备当前运行状态制定维修计划，可避免维修不足与维修过剩等问题。性能退化程度量化评估是实现滚动轴承状态维修的基础。提取滚动轴承早期无故障振动信号的TESPAR参数中的S矩阵作为原始特征，利用主分量分析对其进行降维处理后构建特征矢量，并建立无故障轴承高斯混合模型GMM。将轴承后期振动信号的S矩阵经降维处理后输入该GMM模型，得到被测样本与无故障样本之间的量化相似程度，以此建立时间编码对数似然值TELLP作为滚动轴承性能退化定量指标。轴承疲劳试验表明该方法能及时发现轴承早期故障，并且能很好地跟踪故障发展趋势。
상태유수근거설비당전운행상태제정유수계화，가피면유수불족여유수과잉등문제。성능퇴화정도양화평고시실현곤동축승상태유수적기출。제취곤동축승조기무고장진동신호적TESPAR삼수중적S구진작위원시특정，이용주분량분석대기진행강유처리후구건특정시량，병건립무고장축승고사혼합모형GMM。장축승후기진동신호적S구진경강유처리후수입해GMM모형，득도피측양본여무고장양본지간적양화상사정도，이차건립시간편마대수사연치TELLP작위곤동축승성능퇴화정량지표。축승피로시험표명해방법능급시발현축승조기고장，병차능흔호지근종고장발전추세。
Condition-based maintenance (CBM)as a new maintenance philosophy can avoid the occurrence of insufficient and excessive maintenance efforts.For the purpose of quantitative assessment of bearing performance degradation underlying CBM,a GMM (Gauss mixture model)and TESPAR (time encoded signal processing and recognition)based approach is formulated.The S matrices in the TESPAR parameters of the bearing signals collected from fault-free stage are extracted as the original features,and dimensionally reduced with principal component analysis (PCA)to construct the feature vectors;and a GMM model for the fault-free bearing is established and trained with the S matrices.The S matrices from subsequent bearing stages are dimensionally reduced,and then fed to the trained GMM model to obtain the quantitive similarity degree between the fault-free sample and the sample under test.Such similarity degree serves as a fault severity index,which is herein called TELLP (time encoded log-likelihood probability).Experiment results of bearing fatigue test show that the proposed method is able to detect incipient bearing faults and can track the fault progress trend well.