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中文摘要：
提出一种基于遗传算法的自适应共振解调技术并应用于滚动轴承的早期、微弱故障的精密诊断。传统的共振解调方法需要提前获得带通滤波中心频率和带宽等信息,往往需要反复调试才能获得满意的结果。在传统共振解调的基础上,利用滚动轴承故障特征频率的变化规律,设计了一种新的目标函数,借助遗传算法优良的全局搜索能力确定出最佳的带通滤波参数。在此基础上再进行共振解调分析,可在复杂的干扰噪声中提取出微弱的故障信号,准确识别出非损伤性轻微故障。在轮对跑合试验台上进行检验后发现：该方法可自动地优化出最佳的带通滤波中心频率和带宽,从而精确诊断出早期、非损伤性轴承故障。
英文摘要：
An adaptive resonant demodulation technology based on Genetic Algorithm（GA） is proposed for failure diagnosis of rolling bearing early and weak faults in this paper. For using resonant demodulation technology, additional information including center frequency and bandwidth are needed and the information also has to be adjusted frequently. On the basis of traditional resonant demodulation technology, a new objective function is designed with the help of change rule in the fault characteristic frequency of rolling bearing. Then, the best parameters of band-pass filters can be got by using GA with excellent global search ability. After that, the useful fault information can be extracted from the complex noise through the analysis of resonant demodulation. At last, an actual signal of roiling bearing is measured and analyzed on a wheelset running test rig. The results show that the new method proposed can optimize the best middle frequency and band width of band-pass filters automatically. As a result, the early and non-invasive faults of rolling bearing can be diagnosed accurately.
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