轴承是旋转机械中的关键部件,相对于故障模式识别,性能退化评估可以更为有效地服务于设备主动维护以实现零停机率。小波包分解可以对信号进行更为精细的刻画,基于统计学习理论的支持向量数据描述是一种具有良好计算性能的单值分类方法。基于此,本文提出了一种基于小波包-支持向量数据描述的轴承性能退化评估方法,该方法以小波包分解的节点能量构成特征向量,仅需要正常状态下的数据样本即可用支持向量数据描述建立知识库,在一定程度上实现了对待测样本退化程度的定量评估。通过应用于轴承不同点蚀大小和其加速疲劳寿命试验的全寿命周期,验证了所提出方法的可行性和有效性。
Bearing is an important unit in rotary machinery, performance degradation assessment is more effective than fault pattern recognition for proactive maintenance realizing near-zero downtime. Wavelet package decomposition (WPD) can depict signal finer, statistical learning theory (SLT) based support vector data description (SVDD) is a one-class classification method holding excellent computing ability. Here, we propose a bearing performance degradation assessment method based on them. This method uses nodes energy of WPD to compose feature vectors, and just needs normal data to build knowledge database using SVDD, then qualitative degradation for test data can be realized. Application results for bearing with different defect size and its whole life time of accelerated life test validated this method’s feasibility and validity.