循环脉冲指数最大化的共振稀疏分解法及应用

针对复杂多变工作环境下滚动轴承产生的微弱故障信息难以提取的问题，充分利用故障冲击的脉冲性与循环周期性，提出了循环脉冲度最大化的共振稀疏分解(RSD)方法。该方法以短时脉冲峰值矩的变异系数作为循环脉冲指数对轴承故障信号脉冲性与周期性进行综合表征。然后以循环脉冲指数最大化作为优化目标，采用多尺度简化粒子群算法对RSD的品质因子进行了组合优化。最后构建了最优RSD循环脉冲谱，实现了滚动轴承故障的自动辨识。仿真结果与动车轴箱轴承的故障诊断应用实例表明，提出的循环脉冲指数最大化的RSD能够有效避免强脉冲干扰造成的共振频带误判问题，实现复杂工况环境下的滚动轴承复合多故障同步诊断，具有良好的工程适用性。

A resonance sparse decomposition method based on maximizing cyclic pulse index and its application

It is difficult to extract the weak information of rolling bearing fault under complex and changeable operating environments. To address this issue, an optimized resonance sparse decomposition (RSD) based on maximizing the cyclic pulse index (CPI) is proposed, which takes full advantage of pulse characteristics and cycle period characteristics of fault impacts. Firstly, the variation coefficient of the short-time pulse peak moment is used as the CPI to comprehensively characterize the pulse and periodicity of the bearing fault signal. Then, the quality factors of RSD are optimized by the multi-scale simplified particle swarm optimization algorithm with the objective of maximizing the CPI. Finally, the cyclic pulse spectrum of the low-frequency resonance component is established to automatically identify the bearing faults. The results of simulation and applications in the fault diagnosis of EMU axle box bearings show that the proposed method effectively avoids the misjudgment of resonance frequency band caused by strong pulse interferences, and performs well in the synchronous diagnosis of bearing compound faults under complex working conditions, which demonstrates its engineering applicability in the field of bearing fault diagnosis.