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摘要：列车滚动轴承作为列车运行的一个重要元件，它的健康状况直接影响着列车的安全运行，所以针对滚动轴承的安全检查是很关键的。
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这次毕业设计的目的是利用谐波小波与近似熵两种方法对含噪声的振动信号进行分析，有噪声和无噪声振动的区别，以此来判断滚动轴承是否有故障。
设计中运用了小波和近似熵相结合的方法。近似熵是一个从衡量时间序列复杂性的角度出发的反映信号整体特征的指标,其具有计算所需数据短,对确定性信号和随机信号都有效的特点。近似熵对复杂的信号有很强的的处理能力。27792
滚动轴承产生故障时,由于轴承元件受到刚度非线性、接触摩擦、零件间隙和外载荷等的影响,其振动信号往往会表现为非平稳特征,因此,如何从非平稳振动信号中提取滚动轴承故障特征信息,是滚动轴承的故障诊断中的重中之重。
现有的方法对于非平稳性的信号的处理和分析不是很完善，所以我在接下来的解决方案中运用了小波的方法。对滤波重构信号进行小波变换得到包络信号，然后对包络谱细化得到振动幅值谱图，根据细化后的幅值谱图判别轴承故障的小波一包络分析方法。
毕业论文关键词：信号处理；近似熵；小波变换；小波包
Analysis of rolling bearing vibration faults of heavy-haul freight train
Abstract：Rolling bearing is one of the important components of the train operation, its health status directly affects the safe operation of the train, so for the safety inspection of rolling bearing is the key.
The purpose of the thesis is the harmonic wavelet and the analysis of vibration signal containing noise using approximate entropy difference between the two methods, with and without noise vibration, in order to determine whether the fault of rolling bearing.
Design by using the method of combination of wavelet and approximate entropy. Approximate entropy is the overall characteristics of a measure of time series complexity from the angle of the reflected signal index, which has the characteristics of short, effective for deterministic and random signals. Approximate entropy has strong processing ability for complex signal.
Vibration signals Of the fault bearing Performance for non一stationary features because of Stiffness nonlinear,contact friction, Parts clearance and the load .Therefore,how to extract fault characteristic information of rolling bearing from the nonstationary vibration signal is the top Priority of rolling bearing fault diagnosis.
The existing methods for the processing and analysis of nonstationarity is not very perfect, so I used the wavelet method in the solution of the next. Filtering of wavelet transform to get the reconstructed signal envelope signal, and then refine the envelope spectrum obtained vibration amplitude spectrum analysis method based on amplitude spectrum after refinement determine a bearing failure wavelet envelope.
Key Words：Signal processing；approximate entropy；wavelet transform；Wavelet packet
目 录
1绪论 1
1.1 本课题的意义和目的、国内外研究现状、水平和发展趋势 1
1.1.1 课题意义和目的 1
1.1.2 国内外研究现状与水平 2
1.1.3 发展趋势 2
1.2 主要工作安排 3
2列车滚动轴承故障诊断原理及方法 4
2.1 列车滚动轴承振动原理 4
2.2 滚动轴承固有频率、特征频率 7
2.2.1 固有频率 7
2.2.2 频谱结构 8
2.3 列车滚动轴承振动信号的检测 9
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