基于优化品质因子共振稀疏分解的齿轮故障诊断

Gear Fault Diagnosis Based on Optimal Q-factor Resonance-based Sparse Signal Decomposition

在使用共振稀疏分解方法进行齿轮故障诊断时,针对通常情况下人为选择品质因子的问题,提出优化品质因子的共振稀疏分解方法,利用改进的粒子群优化算法,结合高、低共振分量相关系数与峭度,自适应选择符合信号故障特征的优化品质因子。利用优化品质因子对齿轮故障信号进行共振稀疏分解,对分量进行Hilbert包络分析,从而得到齿轮故障特征频率,结果表明此方法能有效分离故障齿轮冲击成分,实现齿轮故障的诊断。

When using the resonance-based sparse signal decomposition for gear fault diagnosis,in order to solve the problem of artificially selecting the Q-factor under normal circumstances,a resonance-based sparse signal decomposition for optimizing the Q-factor is proposed.The improved particle swarm optimization algorithm is used to combine the correlation of high and low resonance components.Coefficients and kurtosis,adaptively select the optimal Q-factor that matches the characteristics of the signal fault.The optimized Q-factor is used to sparsely decompose the gear fault signal,and the Hilbert envelope analysis is performed on the components to obtain the characteristic frequency of the gear fault.The results show that this method can effectively separate the shock component of the fault gear and realize the diagnosis of gear fault.