基于经验模态分解的滚动轴承故障振动信号消噪研究

发布时间：2018-06-08 02:55

本文选题：滚动轴承 + 振动信号　；参考：《湖南科技大学》2012年硕士论文

【摘要】：在复杂多变的工业现场，滚动轴承具有高事故率、故障高危险性。为有效保障生产效率和人员安全，对滚动轴承出现的故障进行高效、快捷、准确的识别和诊断就显得非常重要。然而滚动轴承因运行环境复杂而使得故障诊断中采集的振动信号被噪声湮没，给故障特征提取带来极大的不便，尤其在故障特征微弱或是故障发生早期。有效实现滚动轴承的故障振动信号消噪，并且研究分析适合于滚动轴承故障振动信号的消噪方法，，对有效提高滚动轴承设备状态监测和故障诊断的精度和效率具有重要的意义。首先，对滚动轴承的正常轴承、内圈故障、外圈故障和滚珠故障的振动信号和噪声情况进行对比，分析轴承振动噪声的分布、能量特性。其次，提出了基于自相关和阈值的经验模态分解消噪方法。在分析经验模态分解方法的消噪性能后，采用噪声自相关特性识别噪声模态和并对其阈值处理，以实现信号重构消噪的方法，可以有效识别经验模态分解后各模态分量中噪声占主导的模态分量，和尽可能减少信号重构时有用成分损失。并用仿真信号验证本方法的消噪效果。再次，提出了基于自相关集成经验模态分解消噪和基于自适应的集成经验模态分解消噪方法。采用克服了模态混叠问题的集成经验模态分解方法，结合自相关分选和阈值处理，实现集成经验模态分解的消噪；在分析信号模态中噪声能量的特点，自适应生成阈值实现消噪处理，从而提出自适应的集成经验模态分解消噪。采用仿真信号验证了集成经验模态分解消噪的性能。最后，对滚动轴承内圈故障振动信号和外圈故障振动信号进行消噪分析，并与常用消噪方法作对比，本文所提新方法能有效识别轴承故障特征频率和工频，具有比常用方法更好的消噪效果。本文通过对滚动轴承故障振动信号和噪声分析，使用改进的经验模态分解消噪方法对滚动轴承故障振动信号进行有效消噪，为滚动轴承状态监测和故障诊断提供有效的信号预处理方法。
[Abstract]:In complex and changeable industrial field, rolling bearing has high accident rate and high fault risk. In order to ensure production efficiency and personnel safety, it is very important to identify and diagnose the faults of rolling bearings efficiently, quickly and accurately. However, because of the complex running environment, the vibration signal collected in fault diagnosis is obliterated by noise, which brings great inconvenience to fault feature extraction, especially in the early stage of fault feature weak or fault. The de-noising of the fault vibration signal of rolling bearing is realized effectively, and the method of de-noising suitable for the fault vibration signal of rolling bearing is studied and analyzed. It is of great significance to improve the accuracy and efficiency of condition monitoring and fault diagnosis of rolling bearing equipment. Firstly, the vibration signal and noise of normal bearing, inner ring fault, outer ring fault and ball fault of rolling bearing are compared. The distribution and energy characteristics of bearing vibration noise are analyzed. Secondly, an empirical mode decomposition de-noising method based on autocorrelation and threshold is proposed. After analyzing the denoising performance of the empirical mode decomposition method, the noise mode is identified by the noise autocorrelation characteristic and the threshold value is processed to realize the de-noising method of signal reconstruction. It can effectively identify the noise-dominated modal components after empirical mode decomposition and minimize the loss of useful components in signal reconstruction. Simulation signals are used to verify the denoising effect of the proposed method. Thirdly, an integrated empirical mode decomposition de-noising method based on autocorrelation and an adaptive integrated empirical mode decomposition de-noising method are proposed. The method of integrated empirical mode decomposition, which overcomes the problem of modal aliasing, is adopted to realize the de-noising of integrated empirical mode decomposition by combining autocorrelation sorting and threshold processing, and the characteristics of noise energy in signal modes are analyzed. Adaptive threshold is generated to realize denoising, and an adaptive integrated empirical mode decomposition (EMD) is proposed. The performance of integrated empirical mode decomposition (EMD) de-noising is verified by simulation signal. Finally, the de-noising signal of inner ring fault vibration signal and outer ring fault vibration signal of rolling bearing is analyzed, and compared with the usual de-noising method. The new method proposed in this paper can effectively identify the bearing fault characteristic frequency and power frequency, and has better denoising effect than the usual method. The vibration signal and noise of rolling bearing fault are analyzed in this paper. The improved empirical mode decomposition (EMD) denoising method is used to effectively de-noise the rolling bearing fault vibration signal, which provides an effective signal preprocessing method for the rolling bearing condition monitoring and fault diagnosis.
【学位授予单位】：湖南科技大学
【学位级别】：硕士
【学位授予年份】：2012
【分类号】：TH165.3;TN911.7

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