基于局部特征尺度分解与复合谱分析的齿轮性能退化特征提取

Gear Performance Degradation Feature Extraction Based on Local Characteristic-scale Decomposition and Modified Composite Spectrum Analysis

针对齿轮性能退化过程中振动信号复杂、特征提取困难等问题,提出采用基于局部特征尺度分解与复合谱的退化特征提取方法。改进复合谱分析法,利用离散余弦变换代替复合谱分析法的傅里叶变换,以减少特征信息的遗漏,提高特征信息敏感度;利用局部特征尺度分解法对振动信号进行分解,并采用贝叶斯信息准则与峭度时间序列互相关系数相结合对内禀尺度分量进行筛选,以剔除不必要分量的影响,有效地提取特征信息;利用改进的复合谱分析法对所选取的内禀尺度分量进行融合,提取复合谱熵作为特征向量。该退化特征提取方法运用于齿轮全寿命退化试验中,对实测信号进行特征提取和退化状态识别,结果表明改进后的复合谱熵对齿轮退化状态具有较好的表征能力。

Since the vibration signal of gear is complicated and degradation features are hard to extract, a novel method based on local characteristic-scale decomposition (LCD) and composite spectrum (CS) entropy is proposed. The CS algorithm is modified, and Fourier transform is replaced by discrete cosine transform so as to reduce the information missing and improve the feature sensitivity. On this basis, LCD-CS algorithm is proposed for degradation feature extraction. Vibration signal is decomposed by using LCD method with high frequency harmonics. Bayesian information criterion and kurtosis time series cross correlation coefficients are used to screen the intrinsic scale components, so as to abandon the unnecessary components and extract the feature information effectively. In order to improve the degradation measurability of features, the selected ISC components are fused by using the modified CS algorithm, and the CS entropy is extracted as feature vector. The proposed method is applied to the gear run-to-failure degradation experiment, and the feature extraction and degenerative status recognition of the measured signals are carried out. The results show that the modified composite spectrum entropy has a good ability to characterize the gear degenerative state.