中文摘要：

基于数据驱动的轴承寿命预测方法主要采用轴承信号的滤波系数、时域和频域的统计量等作为反映轴承性能退化的特征量.进一步研究发现,这些指标要么对轴承早期缺陷敏感度不够,要么缺乏寿命预测所需要的上升或下降的性能退化特征.由于轴承在制造和工作过程中存在较多随机因素,即使同一种轴承在同一种工况下的工作寿命也会存在差异.因此,寻找一种稳定有效的特征值来刻画轴承的退化状态是十分重要的.提出基于时频图像融合的轴承性能退化特征提取方法.采用平滑伪威格纳-维尔分布（Smoothed pseudo Wigner-Ville distribution,SPWVD）表征轴承振动信号的时频能量分布特征,并利用灰度共生矩阵的统计特征作为轴承的性能退化特征;同时利用图像融合方法将同一轴不同方向的振动信号加以综合利用,消除一些随机因素对性能退化特征的影响.采用2012 PHM Competition的轴承数据集,验证了该方法在轴承的性能退化特征提取中的有效性.

英文摘要：

Filter coefficient, time domain and frequency domain statistics are mainly used to describe the bearing performance degradation in bearing life prediction method based on data driven. Further studies show these features have neither sufficient sensitivity nor ascending or descending trend that lifetime prediction needs. The service lifetime of the same type of bearing varies considerably even on the same operating condition due to some random factor occurs in bearing manufacturing and working process. Therefore, searching for stable and effective features to describe the instant degradation of bearing is very important. A feature extraction method of bearing performance degradation based on time-frequency image fusion is proposed. The time-frequency energy distribution of bearing vibration signal is extracted by smoothed pseudo Wigner-Ville distribution（SPWVD）, and the bearing performance degradation is described using the statistical feature calculated by gray level co-occurrence matrix（GLCM）. Also, image fusion is used for combining the two directions of bearing vibration signals to eliminate the influence of the random factor. The result reveals that this method is applicable in the feature extraction of bearing performance degradation using 2012 PHM Competition bearing data sets.