基于信号共振稀疏分解和最大相关峭度解卷积的齿轮箱故障诊断

当齿轮箱内旋转零件发生故障时，其振动信号中的故障脉冲成分易被箱体中其他旋转部件的谐波信号和背景噪声所淹没，故障特征难以被有效提取。针对这一问题，提出了基于信号共振稀疏分解和最大相关峭度解卷积的故障诊断方法。该方法首先通过信号共振稀疏分解将信号中的低共振冲击成分从谐波分量和噪声中分离，然后对低共振分量进行最大相关峭度解卷积计算，进一步突出低共振分量中的周期脉冲成分，最后通过包络谱分析进行故障诊断。算法仿真、实验分析和工程应用结果表明,该方法能够有效提取强噪声信号中的周期性冲击成分，凸显故障特征，从而提供准确可靠的诊断结果。

Gearbox Fault Diagnosis Based on RB-SSD and MCKD

When a rotating part of the gearbox failed, the periodic fault impulse components in the vibration signals were easily submerged by the harmonic components and the strong background noises, thus leading to the challenging difficulties in the fault feature extractions. To address this issue, a new fault diagnosis method was proposed based on RB-SSD and MCKD herein. Firstly, the low-resonance components of the signals were separated from the harmonic components and the noises using RB-SSD method, and then the MCKD method was employed to the low-resonance components to further highlight the periodic impulse components. Finally, the envelope spectrum analysis was performed to diagnose the faults. The evaluation results from simulations, experiments and engineering applications demonstrate that the proposed method may extract effectively and highlight the periodic fault impulse components from noisy vibration signals, thus providing accurate and reliable diagnosis results.