基于Alaph稳定分布与多重分形分析的齿轮箱故障特征提取方法研究进展

齿轮箱工作环境恶劣，齿轮与滚动轴承等关键部件易发生疲劳故障。将目前常用的故障特征提取方法应用于齿轮箱实际诊断时，其结果具有不稳定性。Alpha稳定分布与多重分形分析被逐渐应用于故障诊断领域，这2种方法各具优点且相互关联。文章对Alpha稳定分布及多重分形分析应用于齿轮箱齿轮或滚动轴承故障特征提取的已有成果进行详细梳理，分别从基于Alpha稳定分布的故障特征提取方法、基于多重分形的故障特征提取方法及基于Alpha稳定分布与多重分形的特征融合方法3方面进行评述, 并指出今后可进一步在特征筛选、特征融合等方面开展研究。

Research Progress of Fault Feature Extraction Method of Gear Box Based on Alpha Stable Distribution and Multi-fractal Analysis

Gearbox is usually designed to operate on complex conditions with variable speeds, loading and temperatures, which easily lead to fatigue faults of its key components, such as gears and rolling bearings. At present, the diagnosis result is occasionally unstable when the common fault feature extraction methods are used in actual diagnosis of gearbox. Recently, Alpha stable distribution (ASD) and Multi-fractal analysis (MFA) have been investigated to address this limitation. These two methods have their respective advantages, and can be compensated each other. This article reviews and analyzes the existing achievements, and discusses the three aspects of the fault feature extraction method based on ASD, the fault feature extraction method based on MFA, and the fault feature extraction method based on feature fusion of ASD and MFA, respectively. Finally, the future research directions in feature selection, feature fusion are pointed out.