Yi Cai, Lin Jianhui, Zhang Weihua, Ding Jianming
State Key Laboratory of Traction Power, Southwest Jiaotong University, Chengdu 610031, China.
Sensors (Basel). 2015 May 11;15(5):10991-1011. doi: 10.3390/s150510991.
As train loads and travel speeds have increased over time, railway axle bearings have become critical elements which require more efficient non-destructive inspection and fault diagnostics methods. This paper presents a novel and adaptive procedure based on ensemble empirical mode decomposition (EEMD) and Hilbert marginal spectrum for multi-fault diagnostics of axle bearings. EEMD overcomes the limitations that often hypothesize about data and computational efforts that restrict the application of signal processing techniques. The outputs of this adaptive approach are the intrinsic mode functions that are treated with the Hilbert transform in order to obtain the Hilbert instantaneous frequency spectrum and marginal spectrum. Anyhow, not all the IMFs obtained by the decomposition should be considered into Hilbert marginal spectrum. The IMFs' confidence index arithmetic proposed in this paper is fully autonomous, overcoming the major limit of selection by user with experience, and allows the development of on-line tools. The effectiveness of the improvement is proven by the successful diagnosis of an axle bearing with a single fault or multiple composite faults, e.g., outer ring fault, cage fault and pin roller fault.
随着时间的推移，列车载重和行驶速度不断提高，铁路车轴轴承已成为关键部件，需要更高效的无损检测和故障诊断方法。本文提出了一种基于总体经验模态分解（EEMD）和希尔伯特边际谱的新颖自适应程序，用于车轴轴承的多故障诊断。EEMD克服了常对数据和计算量进行假设的局限性，这些局限性限制了信号处理技术的应用。这种自适应方法的输出是本征模态函数，对其进行希尔伯特变换以获得希尔伯特瞬时频率谱和边际谱。然而，并非分解得到的所有本征模态函数都应纳入希尔伯特边际谱。本文提出的本征模态函数置信指数算法完全自主，克服了由有经验的用户进行选择的主要限制，并有助于开发在线工具。通过成功诊断出具有单一故障或多种复合故障（如外圈故障、保持架故障和滚针故障）的车轴轴承，证明了改进方法的有效性。
