基于小波-能量模式的回转支承故障诊断方法研究与应用

Slewing bearing analysis on fault diagnosis based on wavelet and energy fault mode and its application

回转支承机械结构和工作条件特殊,导致其故障机制复杂,传统的信号分析方法难以对其进行有效的故障诊断。提出了一种基于小波分解与能量谱相结合的回转支承故障诊断方法。利用小波多尺度、多分辨率的特性,对回转支承振动信号进行多尺度分解;根据回转支承低频故障特性,对小波分解后的低频区进行频谱分析,再结合各尺度频带能量谱进行回转支承故障诊断。通过对回转支承加速寿命试验中各阶段数据分析表明,该方法能够有效、准确地诊断出回转支承故障模式,相比单一的小波频谱分析诊断精度更高、可靠性更好,具有一定的工程实用价值。

Traditional signal process method was difficult to be efficiently applied to the slewing bearing fault diagnosis due to the complex failure mechanism caused by the particular mechanical structure and special working conditions. A diagnosis method based on wavelet decomposition and energy spectrum was proposed. A multi-scale and multi-resolution attributes of wavelet were used to decompose the vibration of slewing bearing into different frequency bands. According to the low frequency characteristic of slewing bearing,the specific low frequency band spectrum was selected to analyze and combined with the each scale energy spectrum by wavelet decomposition to diagnose. Through accelerating life experiments,the vibration signal of each process stage of slewing bearing was analyzed,and results showed that the method could be used more effectively and accurately for diagnose the slewing bearing failure mode than the single wavelet spectrum analysis. It had potential engineering applications.