【摘要】：作為風(fēng)力發(fā)電機的重要傳動部件，齒輪箱是風(fēng)力發(fā)電機組中故障率較高的部件之一，因此研究風(fēng)力發(fā)電機齒輪箱故障診斷方法尤為重要。 本文首先介紹了風(fēng)力發(fā)電的國內(nèi)外發(fā)展?fàn)顩r及風(fēng)力機齒輪箱故障診斷方法的研究現(xiàn)狀，闡述了時域指標(biāo)診斷方法的基本理論，簡述了去趨勢波動分析及多重分形去趨勢波動分析的基本原理。針對去趨勢波動分析方法中區(qū)間長度選取的隨機性和主觀性，根據(jù)互信息的基本理論，提出采用符號分析的方法進(jìn)行互信息計算，對去趨勢波動分析的長程相關(guān)性指數(shù)進(jìn)行優(yōu)化，避免了區(qū)間長度對長程相關(guān)性指數(shù)的影響，，確保了去趨勢波動分析改進(jìn)算法的穩(wěn)定性。 然后根據(jù)信號故障時會出現(xiàn)多重分形的特征，通過計算分形圖偏離程度，得到圖形偏離程度（DGD）作為故障識別的時域指標(biāo)。進(jìn)行了滾動軸承的信號仿真，對仿真信號進(jìn)行去趨勢波動分析改進(jìn)算法運算，得到仿真信號的分形圖和DGD特征因子，根據(jù)所得分形圖和DGD特征因子來分辨正常與故障仿真信號。通過對仿真信號的分辨來證明了DGD特征因子的有效性和準(zhǔn)確性。在仿真信號中加入不同程度的噪聲，驗證噪聲對DGD特征因子的影響，證明了噪聲的加入不影響DGD特征因子對故障識別。 最后將去趨勢波動分析改進(jìn)算法應(yīng)用到實際的信號中，對齒輪箱振動信號和風(fēng)力機齒輪箱振動信號進(jìn)行分析，分別求取其分形圖和DGD特征因子，并將DGD特征因子與其他時域指標(biāo)進(jìn)行對比。通過對比發(fā)現(xiàn)，峭度在對實際故障識別的過程中出現(xiàn)了不穩(wěn)定性，而DGD特征因子對風(fēng)力機齒輪箱故障識別上有很好的穩(wěn)定性，證明了本文提出的DGD特征因子對風(fēng)力機齒輪箱故障識別上的優(yōu)越性。
[Abstract]:As an important transmission part of wind turbine, gearbox is one of the components with high failure rate in wind turbine generator, so it is very important to study the fault diagnosis method of wind turbine gearbox. This paper first introduces the development of wind power generation at home and abroad and the research status of wind turbine gearbox fault diagnosis method, and expounds the basic theory of time domain index diagnosis method. The basic principles of detrend fluctuation analysis and multifractal detrend fluctuation analysis are briefly introduced. Aiming at the randomness and subjectivity of interval length selection in the detrend fluctuation analysis method, according to the basic theory of mutual information, a symbolic analysis method is proposed to calculate mutual information. The long range correlation index of detrend volatility analysis is optimized to avoid the influence of interval length on long range correlation index and the stability of the improved algorithm is ensured. Then according to the feature of multifractal when signal fault occurs, the deviation degree of fractal graph (DGD) is obtained as the time domain index of fault identification by calculating the degree of deviation of fractal graph. The signal simulation of rolling bearing is carried out, and the improved algorithm of de-trend fluctuation analysis is applied to the simulation signal. The fractal figure and DGD characteristic factor of the simulation signal are obtained, and the normal and fault simulation signals are distinguished according to the score diagram and the DGD characteristic factor. The validity and accuracy of DGD feature factor are proved by the resolution of simulation signal. The effect of noise on DGD feature factor is verified by adding noise to the simulation signal. It is proved that the addition of noise does not affect the DGD feature factor for fault identification. Finally, the improved algorithm of detrend fluctuation analysis is applied to the actual signal, the vibration signal of the gear box and the vibration signal of the gear box of the wind turbine are analyzed, and the fractal diagram and the DGD characteristic factor are obtained, respectively. The DGD characteristic factor is compared with other time domain indexes. Through comparison, it is found that kurtosis is unstable in the process of actual fault identification, and DGD characteristic factor has good stability for wind turbine gearbox fault identification. It is proved that the DGD characteristic factor proposed in this paper is superior to the wind turbine gearbox fault identification.
【學(xué)位授予單位】：華北電力大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TM315

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