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作 者:唐斌[1] 池茂儒[1] 赵明花 李大柱 许文天 TANG Bin;CHI Maoru;ZHAO Minghua;LI Dazhu;XU Wentian(State Key Laboratory of Rail Transit Vehicle System,Southwest Jiaotong University,Chengdu 610031 Sichuan,China;National Innovation Center of High Speed Train,Qingdao 266000 Shandong,China)
机构地区:[1]西南交通大学轨道交通运载系统全国重点实验室,成都610031 [2]国家高速列车技术创新中心,山东青岛266000
出 处:《铁道机车车辆》2024年第4期84-91,共8页Railway Locomotive & Car
基 金:国家自然科学基金区域创新发展联合基金(U21A20168)。
摘 要:对于滚动轴承的故障检测,提出了一种基于带自适应噪声的改进完全集合经验模态分解(ICEEMDAN)和共振解调的轴承故障检测方法。通过ICEEMDAN算法,把原始振动信号分解为若干个IMF分量;选取有效IMF分量进行求和,得到重构信号;使用快速峭度图法确定共振频带,然后以此设计相应滤波器进行滤波;使用形态学滤波方法进行共振信号的解调,然后再利用FFT得到轴承的故障特征频谱图。内、外圈故障振动数据验证结果表明,该方法能够检测出滚动轴承的故障。Rolling bearings failure detection method is proposed based on improved complete ensemble empirical mode decomposition with adaptive noise(ICEEMDAN)and resonance demodulation.First,the original vibration signal is decomposed into several IMF components by ICEEMDAN algorithm;the effective IMF components are selected to sum up and the reconstruction signal is obtained;the fast computation of a kurtogram is used to determine the resonance band,and then the corresponding filter is designed to filter;the morphological filtering method is used to carry out demodulation of resonance signal,and FFT is used to obtain the fault characteristic spectrum of bearings.The verification results of fault vibration data of inner and outer ring show that the method can detect the faults of rolling bearings.
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