风电机组齿轮箱故障特征提取技术的研究

发布时间：2019-05-17 22:16

【摘要】：随着风力发电技术的发展,风力发电机组的单机容量越来越大,对其设备故障诊断的实时性、准确性及有效性的要求也越来越高。风电机组中的齿轮箱是一动力传送部件,也是一高故障发生率部件,进行风电机组齿轮箱故障诊断的研究,对保证风力发电机组的正常运行具有重要意义。然而,故障振动信号特征信息的提取是风电机组齿轮箱故障诊断研究中的关键,它直接影响了风机齿轮箱故障诊断的准确性、有效性以及故障早期预警的可靠性。本文通过对风电机组齿轮箱故障的类型、原因以及不同故障信号的特征表现进行分析,给出了齿轮箱振动信号的数学模型,并给出了风机齿轮箱故障特征信号提取的方法。以风电机组齿轮箱故障振动信号的非平稳时变特性为出发点,首先,为了更有效地提取其特征信号,提出了基于小波降噪的预处理方法,给出了小波降噪算法；其次,在深入地研究了Hilbert变换和EMD分解技术后,提出了改进的Hilbert变换包络解调法,以实现故障特征的提取,该方法采用EMD技术把齿轮箱故障振动的非平稳时变信号分解成若干IMF(本征模态函数)的线性组合,再对其IMF分量进行Hilbert变换包络解调,从其包络信号的频谱分析获得故障信号特征；最后,通过算例分析验证了该方法能凸显倍频现象,减小故障特征误差,有效地提取了风机齿轮箱的故障特征,具有较高的实用性、准确性、有效性及可靠性,是一种更加合理的故障特征提取方案。
[Abstract]:With the development of wind power generation technology, the single machine capacity of wind turbine is getting larger and larger, and the requirements for real-time, accuracy and effectiveness of equipment fault diagnosis are getting higher and higher. The gearbox in wind turbine is not only a power transmission component, but also a high fault incidence component. The research on fault diagnosis of wind turbine gearbox is of great significance to ensure the normal operation of wind turbine. However, the extraction of fault vibration signal feature information is the key to the fault diagnosis of wind turbine gearbox, which directly affects the accuracy and effectiveness of fan gearbox fault diagnosis and the reliability of fault early warning. Based on the analysis of the types and causes of gearbox faults of wind turbine gearboxes and the characteristics of different fault signals, the mathematical model of gearbox vibration signals is given, and the method of extracting fault characteristic signals of wind turbine gearboxes is given. Based on the non-stationary time-varying characteristics of the fault vibration signal of wind turbine gearbox, firstly, in order to extract the characteristic signal more effectively, a preprocessing method based on wavelet noise reduction is proposed, and the wavelet noise reduction algorithm is given. Secondly, after deeply studying the Hilbert transform and EMD decomposition technology, an improved Hilbert transform envelope demodulation method is proposed to realize the fault feature extraction. In this method, the non-stationary time-varying signal of fault vibration of gearbox is decomposed into linear combination of several IMF (intrinsic modal function) by EMD technique, and then the IMF component is Demodulated by Hilbert transform envelope. The fault signal characteristics are obtained from the spectrum analysis of the envelope signal. Finally, an example is given to verify that the method can highlight the frequency doubling phenomenon, reduce the error of fault characteristics, effectively extract the fault characteristics of fan gearbox, and has high practicability, accuracy, effectiveness and reliability. It is a more reasonable fault feature extraction scheme.
【学位授予单位】：华北电力大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TM315

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