風電機組齒輪箱故障特征提取技術的研究
發(fā)布時間：2019-05-17 22:16
【摘要】：隨著風力發(fā)電技術的發(fā)展,風力發(fā)電機組的單機容量越來越大,對其設備故障診斷的實時性、準確性及有效性的要求也越來越高。風電機組中的齒輪箱是一動力傳送部件,也是一高故障發(fā)生率部件,進行風電機組齒輪箱故障診斷的研究,對保證風力發(fā)電機組的正常運行具有重要意義。 然而,故障振動信號特征信息的提取是風電機組齒輪箱故障診斷研究中的關鍵,它直接影響了風機齒輪箱故障診斷的準確性、有效性以及故障早期預警的可靠性。 本文通過對風電機組齒輪箱故障的類型、原因以及不同故障信號的特征表現(xiàn)進行分析,給出了齒輪箱振動信號的數(shù)學模型,并給出了風機齒輪箱故障特征信號提取的方法。以風電機組齒輪箱故障振動信號的非平穩(wěn)時變特性為出發(fā)點,首先,為了更有效地提取其特征信號,提出了基于小波降噪的預處理方法,給出了小波降噪算法；其次,在深入地研究了Hilbert變換和EMD分解技術后,提出了改進的Hilbert變換包絡解調法,以實現(xiàn)故障特征的提取,該方法采用EMD技術把齒輪箱故障振動的非平穩(wěn)時變信號分解成若干IMF(本征模態(tài)函數(shù))的線性組合,再對其IMF分量進行Hilbert變換包絡解調,從其包絡信號的頻譜分析獲得故障信號特征；最后,通過算例分析驗證了該方法能凸顯倍頻現(xiàn)象,減小故障特征誤差,有效地提取了風機齒輪箱的故障特征,具有較高的實用性、準確性、有效性及可靠性,是一種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.
【學位授予單位】：華北電力大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TM315
本文編號：2479447
[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.
【學位授予單位】：華北電力大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TM315
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