摘要
为了解决轮毂电机轴承早期微弱故障特征难以提取的问题,提出一种基于优化奇异谱分解(optimized singular spectrum decomposition,OSSD)和增强多点最优调整最小熵解卷积(enhance multipoint optimal minimum entropy deconvolution adjusted,EMOMEDA)的特征提取方法,以实现故障特征的检测与提取,及时掌握轮毂电机的运行安全。首先,提出由新的时频综合指标(time-frequency composite index,TCI)自适应优化分量个数的OSSD方法,并对原始信号进行前处理,通过包络谱峰值指标选择敏感的奇异谱分量。然后,提出EMOMEDA方法,设计一种改进的波形延拓策略恢复解卷积信号长度,克服MOMEDA算法的边缘效应,并通过二次解卷积运算获得最优解卷积信号。最后,对最优解卷积信号进行包络分析,实现故障特征的增强提取。分别采用仿真和试验信号验证所提方法的可行性,并将其与多种故障特征提取方法进行对比,证明了其优越性。结果表明,所提方法能够有效提取微弱故障特征,在特征增强方面具有可观的优势。
To solve the problem that it is difficult to extract the early weak fault features of in-wheel motor bearings,an original feature extraction method based on optimized singular spectrum decomposition(OSSD)and enhance multipoint optimal minimum entropy deconvolution adjusted(EMOMEDA)is proposed to realize the fault detection and extraction,then timely grasp the operation safety of the in-wheel motor.Firstly,an OSSD method that adaptively optimizes the number of components by a new time-frequency composite index(TCI)is proposed which is used to pre-process the original signal,and the sensitive singular spectrum component(SSC)is selected by the envelope spectral peak index(ESPI).Then the EMOMEDA method is proposed.An improved waveform continuation strategy is designed to restore the length of the deconvolution signal which overcomes the edge effect of the MOMEDA algorithm,and the optimal deconvolution signal is obtained through the second deconvolution operation.Finally,the envelope analysis is performed on the optimal deconvolution signal to realize the enhanced extraction of fault features.The feasibility of the proposed method is verified by simulation and test signals respectively,and its superiority is proved by comparing it with a variety of fault feature extraction methods.The results show that the proposed method can effectively extract weak fault features and has considerable advantages in feature enhancement.
作者
丁殿勇
薛红涛
刘炳晨
DING Dianyong;XUE Hongtao;LIU Bingchen(School of Automotive and Traffic Engineering,Jiangsu University,Zhenjiang 212013,Jiangsu Province,China)
出处
《中国电机工程学报》
EI
CSCD
北大核心
2023年第24期9721-9732,共12页
Proceedings of the CSEE
基金
国家自然科学基金项目(51775245)。
关键词
轮毂电机
轴承故障
特征提取
奇异谱分解
多点最优调整最小熵解卷积
in-wheel motor
bearing faults
feature extraction
singular spectrum decomposition
multipoint optimal minimum entropy deconvolution adjusted
