【摘要】：近年我國風電行業(yè)飛速發(fā)展,至2012年全國累計風電裝機容量已達75.32GW,在全球風電裝機容量中占據(jù)了第一的位置。但風電機組故障頻繁發(fā)生,導致運行維護費用增加。而風電機組齒輪箱又是風電機組中故障率較高的部件,且維修、更換困難,因此,對于風電機組齒輪箱故障診斷分析方法、故障特征值提取方面的研究具有較大的經(jīng)濟價值和現(xiàn)實意義。 小波分析方法是近年來一門迅速發(fā)展的技術,廣泛應用于信號分析、圖像處理和通信等工程領域,本文以風電機組齒輪箱為研究對象,采用小波分析為工具對其故障特征值提取方面進行了理論研究和實例分析。 本文首先對小波理論包括連續(xù)小波變換、離散小波變換和小波包進行了總結,并給出仿真分析案例；其次,對小波降噪的各種方法和小波包能量法進行了研究,并通過實際信號的分析對該方法的有效性進行了驗證；第三,探討了一種較為新穎的小波分析方法,即復數(shù)小波多尺度包絡譜分析方法,該方法能多尺度分解信號,利用不同的頻率覆蓋范圍提取和分離機械故障信號的包絡譜,克服了傳統(tǒng)包絡譜分析方法中必需預知故障頻帶的缺點,將帶通濾波和包絡分析一步完成,提高了信號分析的魯棒性,并用風電機組實測信號進行了測試,證實了復數(shù)小波多尺度包絡譜分析方法的有效性；最后,針對第一代小波變換的局限性,探討了第二代小波的構造方法,并通過Matlab軟件編寫程序構造第二代小波,應用于風電機組實際信號的分析,證明了第二代小波在信號分析中的可行性。 小波分析作為近年快速發(fā)展的新興領域,理論深刻且應用廣泛,在風電機組齒輪箱的故障特征值提取中得到了較好的應用,并且在其它機械故障診斷領域具有很好的應用前景,是一項非常值得推廣的技術。
[Abstract]:In recent years, the wind power industry in China has developed rapidly, and the total installed wind power capacity has reached 75.32 GW in 2012, which occupies the first position in the global wind power installed capacity. However, wind turbine failures frequently occur, resulting in increased operating and maintenance costs. The gearbox of wind turbine is the part with high failure rate, and it is difficult to repair and replace. Therefore, the method of fault diagnosis and analysis for the gearbox of wind turbine, The study of fault eigenvalue extraction is of great economic value and practical significance. Wavelet analysis is a rapidly developing technology in recent years. It is widely used in signal analysis, image processing, communication and other engineering fields. Wavelet analysis is used to extract the fault eigenvalue. In this paper, the wavelet theory including continuous wavelet transform, discrete wavelet transform and wavelet packet is summarized, and a simulation case is given. Secondly, various methods of wavelet noise reduction and wavelet packet energy method are studied. The validity of the method is verified by the analysis of actual signals. Thirdly, a novel wavelet analysis method, complex wavelet multi-scale envelope spectrum analysis method, is discussed, which can decompose signals at multiple scales. By using different frequency coverage to extract and separate the envelope spectrum of mechanical fault signal, this paper overcomes the shortcoming of the traditional envelope spectrum analysis method which must predict the fault frequency band, and completes the band-pass filter and envelope analysis in one step. The robustness of signal analysis is improved, and the validity of complex wavelet multi-scale envelope spectrum analysis method is verified by testing the measured signal of wind turbine. Finally, aiming at the limitation of the first generation wavelet transform, The construction method of the second generation wavelet is discussed, and the second generation wavelet is constructed by the Matlab software, which is applied to the analysis of the actual signal of the wind turbine, and proves the feasibility of the second generation wavelet in the signal analysis. Wavelet analysis, as a new field of rapid development in recent years, has deep theory and wide application. It has been applied in the extraction of fault eigenvalue of wind turbine gearbox, and has a good application prospect in other fields of mechanical fault diagnosis. It is a technique worth popularizing.
【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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