基于DRS与改进Autogram的风电齿轮箱复合故障特征提取

Compound Fault Feature Extraction of Wind Power Gearbox Based on DRS and Improved Autogram

复合故障特征提取是分析风电齿轮箱故障根因的关键。提出基于离散随机分离(DRS)和改进Autogram的复合故障特征提取方法。基于DRS方法削弱振动信号周期性成分对微弱故障成分的影响,结合谱峭度与谱负熵设计一种新的特征量化指标,对最大重叠离散小波包变换与无偏自相关处理后的各窄带分量进行综合评价,以选择最优的滤波频带,精确地识别包含复合故障特征的信号分量。将所提方法应用于实际风电齿轮箱齿轮-轴承复合故障诊断中,能够有效提取出振动信号中的多个故障特征,具有较好的诊断效果。

Compound fault feature extraction is the key to analyzing the root cause of wind power gearbox faults.A compound fault feature extraction method based on DRS and improved Autogram is proposed.Based on the DRS method,the influence of the periodic components of vibration signals on the weak fault components is reduced.A new feature quantification index of spectral kurtosis and spectral negative entropy is designed to comprehensively evaluate the narrow-band components after maximum overlapping discrete wavelet packet transform and unbiased autocorrelation processing,so as to select the optimal filtering frequency band and accurately identify the signal components containing compound fault features.The method in this paper is applied to the compound fault diagnosis of wind power gearbox and bearing,which can effectively extract multiple fault features from vibration signals and has a good diagnostic effect.