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作 者:宁善平 赵晨 武文星 Ning Shanping;Zhao Chen;Wu Wenxing(School of Rail Transit,Guangdong Communication Polytechnic,Guangzhou 510630,China)
机构地区:[1]广东交通职业技术学院轨道交通学院,广州510630
出 处:《机电工程技术》2024年第11期215-219,共5页Mechanical & Electrical Engineering Technology
基 金:广东省教育厅青年创新人才基金(2022KQNCX191)。
摘 要:针对振动信号中包含瞬时冲击成分、背景噪声、谐波成分等多种重叠特性难以有效识别这一问题,提出了一种改进ISTA算法的高速列车轴箱轴承故障诊断方法。首先对原始信号采用相关滤波法获取与振动故障信号最优的小波原子,构建了稀疏表示的过完备字典。然后,针对ISTA在强噪声背景下重构单个的Laplace小波会出现严重畸变,修正阈值收缩函数,改善了稀疏表示系数的稀疏性,提高了故障振动信号的重构能力。最后,仿真信号和实验表明:改进后ISTA算法在信号重构方面清晰还原了原始信号,很好地抑制了背景噪声,同时在故障特征提取方面有更优的特征提取能力,包络分析中故障特征频率成分也得到了较好的增强,有效实现了轴承外圈的故障诊断,对高速列车轴箱轴承的故障诊断有一定的实际意义。Addressing the issue of effectively identifying the overlapping characteristics in vibration signals such as transient shock components,background noise,and harmonic components,an improved ISTA algorithm for fault diagnosis of high-speed train axle box bearings is proposed.Firstly,the optimal wavelet atom corresponding to the vibration fault signal is obtained from the original signal using the correlation filtering method,and an overcomplete dictionary with sparse representation is constructed.Then,considering that the reconstruction of a single Laplace wavelet by ISTA under a strong noise background can lead to severe distortion,the sparsity of the sparse representation coefficients is improved by modifying the threshold shrinkage function,enhancing the reconstruction ability of the fault vibration signal.Finally,simulation signals and experiments show that the improved ISTA algorithm clearly restores the original signal in terms of signal reconstruction,effectively suppressing background noise.In terms of fault feature extraction,it exhibits superior feature extraction capabilities,and the fault characteristic frequency components are also better enhanced in envelope analysis,effectively achieving fault diagnosis of the bearing outer ring.It has certain practical significance for the fault diagnosis of high-speed train axle box bearings.
关 键 词:轴承 稀疏表示 Laplace小波原子 改进ISTA算法 故障诊断
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