最大相关峭度解卷积结合稀疏编码收缩的齿轮微弱故障特征提取

针对强背景噪声环境下齿轮早期故障诊断问题,提出了最大相关峭度解卷积结合稀疏编码收缩的微弱故障特征提取方法。由于最大相关峭度解卷积算法的处理结果同时受滤波器长度参数及解卷积周期参数的影响,为自适应地实现最佳的解卷积效果,利用粒子群算法优良的寻优特性,对最大相关峭度解卷积算法的最佳影响参数组合进行搜索。原故障信号经影响参数优化的最大相关峭度解卷积算法处理后,冲击特征会明显增强,为剔除剩余噪声,对所获解卷积信号做进一步稀疏编码收缩降噪处理,并通过分析降噪信号的包络谱来识别故障特征频率成分。实例分析结果验证了该方法的有效性和可靠性。

Weak feature extraction of gear fault based on maximum correlated kurtosisdeconvolution and sparse code shrinkage

Aiming at incipient fault diagnosis problem of gear under strong background noise, an feature extraction method for weak fault based on maximum correlated kurtosis deconvolution and sparse code shrinkage was proposed. As the processing result of maximum correlated kurtosis deconvolution algorithm was affected by filter length parameter and deconvolution period parameter, in order to achieve the best deconvolution result adaptively, particle swarm optimization algorithm with excellent optimization characteristic was used to search for the optimal combination of influencing parameters of maximum correlated kurtosis deconvolution algorithm. The impact characteristic of original fault signal could be enhanced after processed by maximum correlated kurtosis deconvolution algorithm with optimized parameters, in order to eliminate the residual noise, the deconvolution signal was further processed by sparse code shrinkage de-noising algorithm, then fault characteristic frequency components could be identified by analyzing the envelope spectrum of de-noising signal. The analysis results verified the effectiveness and reliability of this method.