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  本文關(guān)鍵詞：基于HHT和WNN的齒輪箱故障診斷　出處：《武漢工程大學》2011年碩士論文　論文類型：學位論文

  更多相關(guān)文章： 齒輪箱 故障診斷 Hilbert-Huang變換 經(jīng)驗模態(tài)分解 小波神經(jīng)網(wǎng)絡(luò)

【摘要】：近幾十年來,由于齒輪箱故障診斷的至關(guān)重要而被廣泛研究。本文是基于希爾伯特-黃變換(Hilbert-Huang Transform,簡稱HHT)和小波神經(jīng)網(wǎng)絡(luò)(Wavelet Neural Network,簡稱WNN)的齒輪箱故障診斷,Hilbert-Huang變換是一種新的時頻分析方法,同時也是一種自適應信號處理方法。它包括經(jīng)驗模態(tài)分解(Empirical Mode Decomposition,簡稱EMD)方法和Hilbert譜分析兩個過程。EMD方法是基于信號的局部特征時間尺度,將復雜的信號分解為有限的本征模態(tài)函數(shù)(Intrinsic Mode Function,簡稱IMF)之和;對本征模態(tài)函數(shù)應用Hilbert變換可以得到故障信號的Hilbert譜和Hilbert邊際譜,從而能有效的提取故障特征,識別故障模式,進行故障診斷,這種自適應的分解方法非常適合于非線性和非平穩(wěn)過程的分析。 基于齒輪箱故障振動信號所表現(xiàn)的非線性非平穩(wěn)特征,為了提取齒輪箱中的故障特征信息,本文首先對齒輪箱中采集的振動信號作小波包分解,對信號作降噪處理,同時選取特定頻帶的小波重構(gòu)信號應用Hilbert-Huang變換進行了分析,得到經(jīng)驗模態(tài)分解(EMD)過程和一系列本征模態(tài)函數(shù)(IMF),選擇特定的本征模態(tài)函數(shù)作Hilbert變換,獲取振動信號的Hilbert譜和Hilbert邊際譜,提取故障特征頻率,有效的識別了齒輪箱中齒輪裂紋的不同故障模式。 本文還提出了一種基于混合特征提取和WNN的齒輪箱故障診斷方法,采用時域分析法、小波分解和小波包分解相結(jié)合的方法對齒輪箱振動信號進行故障特征提取,將所提取的特征值作為WNN分類器的特征輸入?yún)��?shù),采用反向傳播(BP)算法對WNN結(jié)構(gòu)中的平移參數(shù)、尺度參數(shù)、連接權(quán)值和閾值進行調(diào)整和優(yōu)化。通過對三種具有不同裂紋尺寸的故障齒輪進行識別和分類,表明WNN有很好的模式識別和分類能力,能很好地應用于旋轉(zhuǎn)機械的故障診斷。
[Abstract]:In the last several ten years, the gearbox fault diagnosis has been widely studied. This paper is based on Hilbert-Huang Transform, which is based on Hilbert-Huang transform. The gearbox fault diagnosis based on HHT and wavelet Neural network. Hilbert-Huang transform is a new time-frequency analysis method. It is also an adaptive signal processing method, which includes empirical Mode Decomposition. EMD method and Hilbert spectrum analysis are two processes. EMD method is based on the local feature of the signal time scale. The complex signal is decomposed into the sum of Intrinsic Mode function (IMF). The Hilbert spectrum and the Hilbert marginal spectrum of the fault signal can be obtained by applying the Hilbert transform to the intrinsic mode function, which can effectively extract the fault features and identify the fault mode. For fault diagnosis, this adaptive decomposition method is very suitable for nonlinear and nonstationary process analysis. Based on the nonlinear non-stationary characteristic of the gearbox fault vibration signal, in order to extract the fault characteristic information from the gearbox, the vibration signal collected in the gearbox is decomposed by wavelet packet. The signal is de-noised, and the wavelet reconstruction signal with special frequency band is analyzed by Hilbert-Huang transform. The empirical mode decomposition (EMD) process and a series of intrinsic mode functions are obtained, and the specific eigenmode functions are selected for Hilbert transformation. The Hilbert spectrum and Hilbert marginal spectrum of vibration signal are obtained, the fault characteristic frequency is extracted, and the different fault modes of gear crack in gear box are effectively identified. A method of gearbox fault diagnosis based on hybrid feature extraction and WNN is presented in this paper. Wavelet decomposition and wavelet packet decomposition are combined to extract the fault features of the gearbox vibration signal. The extracted eigenvalues are used as the input parameters of the WNN classifier. The translation parameter and scale parameter in WNN structure are analyzed by back-propagation algorithm. The connection weights and thresholds are adjusted and optimized. Through the identification and classification of three kinds of fault gears with different crack sizes, it is shown that WNN has a good capability of pattern recognition and classification. It can be used in fault diagnosis of rotating machinery.
【學位授予單位】：武漢工程大學
【學位級別】：碩士
【學位授予年份】：2011
【分類號】：TH165.3

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