基于多指标模糊融合的滚动轴承诊断的最优频带解调方法

Optimal Frequency Band Demodulation for Fault Diagnosis of Rolling Element Bearings Based on Fuzzy Fusion of Multiple Criteria

轴承运行时会产生较大的振动噪声,采用振动信号统计量指标可以识别其共振频带,并通过共振频带解调来提取故障特征信号。目前常用的峭度图等方法根据经验自顶向下粗略划分轴承振动频谱,而且采用单一指标识别共振频带,常常被噪声所干扰,因而鲁棒性不高。为了提高滚动轴承故障诊断的精度,提出一种多指标模糊融合的最优频带解调方法。采用自底向上的思路,以最小化代价函数为条件,通过细分振动频谱,将细分的频谱进行双向合并,可以提高频带划分的精度。在提出的方法中,代价函数由峭度、平滑因子、峰度系数等多个指标应用模糊贴近度方法进行数据融合构造,可以有效提高识别最优共振频带的鲁棒性。分别采用仿真信号和实际采集信号对所提出的方法进行测试,与现有的单指标方法相比,试验结果表明所提出的方法可以正确诊断滚动轴承的故障。

Due to the existence of noises, a statistical criterion of the vibration signal can be used to guide the identification of the resonance frequency band, which is then demodulated for extracting the faulty signature of the bearing. The commonly used resonance demodulation methods, such as the Kurtogram, coarsely portion the vibration band by empirically are used a top-down strategy. Employing only one criterion for the resonance band identification, moreover, is often interfered by the noises and hence being lack of robustness. To improve the accuracy of the bearing fault diagnosis, a fuzzy fusion technique using multiple criteria is proposed for the optimal band demodulation. In the present method, a fine approximation is first generated for the vibration spectrum. The fine spectrum is then merged bidirectionally using a bottom-up strategy. In this way, the frequency band can be segmented precisely to achieve the minimum cost function. In the present method, the cost function is constructed by a fuzzy neartude-based data fusion of kurtosis, smoothness index and crest factor, and therefore leading to the robust resonance band. Both simulated and actual signals are collected for testing the proposed technique. The results show that, comparing to the existing mono-criterion methods, the proposed technique is capable of correctly diagnosing the condition of the rolling element bearings.