摘要
对滚动轴承复合故障进行诊断时,通常采用先分离后诊断的信号处理方法,由于故障特征信号相互耦合或干扰,容易出现误诊或漏诊的现象,针对该问题,提出了基于Autogram的共振解调和1.5维谱的复合故障诊断方法,能够在不分离复合故障信号的前提下识别故障类型。采用变分模态分解(VMD)对原始振动信号降噪,提出了一种综合指标Z选取VMD的有效分量进行信号重构,提高信号的信噪比;使用Autogram算法确定共振频带中心频率和带宽,对共振信号进行包络解调,得到包络信号的1.5维谱,根据1.5维谱中的故障特征来识别滚动轴承复合故障的类型。采用滚动轴承3种不同类型复合故障的实测信号验证了所提方法的可行性,试验结果表明,所提出的方法可以提高复合故障识别的准确性和直观性。
When diagnosing compound faults of rolling bearings,the signal processing method of separation is usually used before diagnosis.Because of mutual coupling or interference of fault characteristic signals,it is easy to misdiagnose or miss diagnosis.Aiming at this problem,a compound fault diagnosis method based on Autogram resonance demodulation and 1.5-dimensional spectrum is proposed,which can identify fault types without separating compound fault signals.The original vibration signal is denoised by using variational mode decomposition(VMD),and a comprehensive index Z is proposed to select the effective components of VMD for signal reconstruction to improve the signal to noise ratio.The Autogram algorithm is used to determine the center frequency and bandwidth of the resonance frequency band,and the envelope demodulation of the resonance signal is carried out to obtain the 1.5-dimensional spectrum of the envelope signal.The types of rolling bearing composite faults are identified according to the fault features in the 1.5-dimensional spectrum.The feasibility of the proposed method is verified by the measured signals of rolling bearings with three different types of compound faults.The experimental results show that the proposed method can improve the accuracy and intuition of composite fault identification,and has certain application value in engineering practice.
作者
王慧滨
剡昌锋
孟佳东
陈光亿
吴黎晓
WANG Hui-bin;YAN Chang-feng;MENG Jia-dong;CHEN Guang-yi;WU Li-xiao(School of Mechanical and Electrical Engineering,Lanzhou University of Technology,Lanzhou 730050,China;Department of Medical Technology,Zhangzhou Health Vocational College,Zhangzhou 363000,China;Institute of Railway Technology,Lanzhou Jiaotong University,Lanzhou 730030,China)
出处
《振动工程学报》
EI
CSCD
北大核心
2022年第6期1541-1551,共11页
Journal of Vibration Engineering
基金
国家自然科学基金资助项目(51765034)。
