ISSN1006－7167CN31－1707/TＲESEAＲCHANDEXPLOＲATIONINLABOＲATOＲY第41卷第12期Vol．41No．122022年12月Dec．2022·仪器设备研制与开发·DOI:10．19927/j．cnki．syyt．2022．12．013基于自相关降噪和局域均值分解的轨道车辆轴箱轴承故障诊断方法宋冬利，董俭雄，郑则君，江炘坤(西南交通大学牵引动力国家重点实验室，成都610031)摘要:以轨道车辆轴箱轴承为研究对象，建立了基于振动监测的故障诊断模型，提出了基于自相关降噪和局域均值分解的轨道车辆轴箱轴承故障特征提取方法，用于轨道车辆轴箱轴承故障分类辨识。为分析该方法在轴箱轴承故障诊断中的有效性，参考轨道车辆轴箱轴承实际服役工况搭建了实验平台。进一步利用加速度传感器采集信号，将不同故障模式下的振动数据按照所构建方法的流程进行故障特征频率提取。实验结果表明，原始信号中的随机干扰噪声得到有效抑制，故障特征3倍频及以上频率成分被成功提取出来，验证了所构建方法用于轨道车辆轴箱轴承故障诊断的可行性。关键词:轴箱轴承;故障诊断;局域均值分解;自相关降噪中图分类号:TH17文献标志码:A文章编号:1006－7167(2022)12－0063－05FaultDiagnosisMethodofAxleBoxBearingofHigh-SpeedTrainBasedonAutocorrelationNoiseＲeductionandLMDSONGDongli，DONGJianxiong，ZHENGZejun，JIANGXinkun(StateKeyLaboratoryofTractionPower，SouthwestJiaotongUniversity，Chengdu610031，China)Abstract:Afaultdiagnosismodelbasedonvibrationmonitoringisproposed，andafaultfeatureextractionmethodbasedonautocorrelationnoisereductionandlocalmeandecompositionisestablishedforfaultclassificationandidentificationofrailvehicleaxleboxbearings．Inordertoanalyzetheeffectivenessofthismethodinaxleboxbearingfaultdiagnosis，anexperimentalplatformisbuiltbyconsideringtheactualserviceconditionsoftheaxleboxbearingofrailvehicles．Thevibrationdataunderdifferentfaultmodesarefurtherextractedaccordingtotheflowofthemethod．Theanalysisoftheexperimentalresultsshowsthattherandominterferencenoiseintheoriginalsignaliseffectivelysuppressed，andthefrequencycomponentsof3timesandaboveofthefaultfeatureareextracted，whichverifiesthefeasibilityoftheproposedmethodforthefaultdiagnosisoftheaxleboxbearingofrailvehicles．Keywords:axleboxbearing;faultdiagn...
