小波分析在风电齿轮箱故障特征提取中的应用研究

发布时间：2018-07-28 18:32

【摘要】：近年我国风电行业飞速发展,至2012年全国累计风电装机容量已达75.32GW,在全球风电装机容量中占据了第一的位置。但风电机组故障频繁发生,导致运行维护费用增加。而风电机组齿轮箱又是风电机组中故障率较高的部件,且维修、更换困难,因此,对于风电机组齿轮箱故障诊断分析方法、故障特征值提取方面的研究具有较大的经济价值和现实意义。小波分析方法是近年来一门迅速发展的技术,广泛应用于信号分析、图像处理和通信等工程领域,本文以风电机组齿轮箱为研究对象,采用小波分析为工具对其故障特征值提取方面进行了理论研究和实例分析。本文首先对小波理论包括连续小波变换、离散小波变换和小波包进行了总结,并给出仿真分析案例；其次,对小波降噪的各种方法和小波包能量法进行了研究,并通过实际信号的分析对该方法的有效性进行了验证；第三,探讨了一种较为新颖的小波分析方法,即复数小波多尺度包络谱分析方法,该方法能多尺度分解信号,利用不同的频率覆盖范围提取和分离机械故障信号的包络谱,克服了传统包络谱分析方法中必需预知故障频带的缺点,将带通滤波和包络分析一步完成,提高了信号分析的鲁棒性,并用风电机组实测信号进行了测试,证实了复数小波多尺度包络谱分析方法的有效性；最后,针对第一代小波变换的局限性,探讨了第二代小波的构造方法,并通过Matlab软件编写程序构造第二代小波,应用于风电机组实际信号的分析,证明了第二代小波在信号分析中的可行性。小波分析作为近年快速发展的新兴领域,理论深刻且应用广泛,在风电机组齿轮箱的故障特征值提取中得到了较好的应用,并且在其它机械故障诊断领域具有很好的应用前景,是一项非常值得推广的技术。
[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.
【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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