Abstract Rolling bearing with a localized defect usually generates periodically impact vibration responses, which carry important information for bearing fault diagnosis. Due to the inevitable noise disturbances, extracting accurate impact features of faulty bearing is still a hard task. In view of the superiority of sparse decomposition on feature extraction, a novel sparse dictionary design method is proposed based on edited cepstrum to improve the precision of feature extraction. The impulse response function is selected as sparse atom, which better reflects the structure and inherent modal characteristics of the faulty bearing. The modal parameters are directly identified from the deconvolved fault signal by edited cepstrum. Identification errors caused by the cepstrum windowing are corrected by quantitative compensation, which further improves the accuracy of dictionary design. A segmental matching pursuit algorithm is applied to speed sparse coefficients solving and fault features reconstruction. A series of simulation analyses verify the proposed method's effectiveness, anti-noise performance and robustness. Experimental tests on pure rolling bearing and gearbox bearing further verify the method's effectiveness under different working conditions. Additionally, comparisons with an improved spectral kurtosis method and an edited cepstrum methodshow the proposed method be more reliable in diagnostic performance.

中文翻译：

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基于编辑倒谱的稀疏字典设计及其在滚动轴承故障诊断中的应用

摘要 具有局部缺陷的滚动轴承通常会产生周期性的冲击振动响应，这些响应为轴承故障诊断提供了重要信息。由于不可避免的噪声干扰，提取故障轴承的准确冲击特征仍然是一项艰巨的任务。针对稀疏分解在特征提取上的优越性，提出了一种基于编辑倒谱的稀疏字典设计方法，以提高特征提取的精度。脉冲响应函数选择为稀疏原子，更能反映故障轴承的结构和固有模态特征。模态参数通过编辑倒谱从解卷积的故障信号中直接识别。由倒谱加窗引起的识别误差通过定量补偿进行校正，这进一步提高了字典设计的准确性。应用分段匹配追踪算法来加速稀疏系数求解和故障特征重建。一系列仿真分析验证了该方法的有效性、抗噪性能和鲁棒性。在纯滚动轴承和齿轮箱轴承上的实验测试进一步验证了该方法在不同工况下的有效性。此外，与改进的谱峰度方法和编辑倒谱方法的比较表明，所提出的方法在诊断性能上更可靠。抗噪性能和鲁棒性。在纯滚动轴承和齿轮箱轴承上的实验测试进一步验证了该方法在不同工况下的有效性。此外，与改进的谱峰度方法和编辑倒谱方法的比较表明，所提出的方法在诊断性能上更可靠。抗噪性能和鲁棒性。在纯滚动轴承和齿轮箱轴承上的实验测试进一步验证了该方法在不同工况下的有效性。此外，与改进的谱峰度方法和编辑倒谱方法的比较表明，所提出的方法在诊断性能上更可靠。