本文關鍵詞：基于包絡分析的齒輪故障監(jiān)測及基于人工神經網絡的齒輪故障分類　出處：《重慶大學》2011年碩士論文　論文類型：學位論文

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【摘要】：齒輪是機械傳動系統(tǒng)中應用最為廣泛的部件,現代科技的發(fā)展使機械設備越來越精密化,給這些設備的狀態(tài)監(jiān)測帶來困難。齒輪箱是機械設備中的重要一員,其工作狀態(tài)的監(jiān)測必須具有實時性和有效性,這樣機械設備中任何故障都能被檢測到,并能在早期得到及時的維修。 包絡分析在軸承和齒輪的故障診斷中已經得到廣泛的應用,傳統(tǒng)方法要求監(jiān)測信號在帶通濾波前需進行時域同步平均處理,以提高包絡分析的效果。然而,在實際應用中,齒輪振動信號的時域同步平局處理很難進行,需要其他的方法來輔助齒輪故障診斷中包絡分析。針對上述問題,本文提出了一種新的解調方法,它對齒輪振動信號進行以共振頻率為中心頻率(一般遠離具有較大幅值的齒輪嚙合頻率諧波分量)的帶通濾波,再進行包絡分析,實現兩種類型,不同損傷程度的齒輪局部故障(輪齒裂紋和剝落)的診斷和分類。齒輪的局部故障將導致一對嚙合的齒輪,每旋轉一圈的過程中會產生低幅脈沖信號,它會和齒輪的結構共振信號產生調制現象。但是這個低能量的信號往往會被淹沒在齒輪箱其他信號源產生的高能量信號中。本文提出了一個方法,它通過從監(jiān)測信號中提取故障激勵信號從而獲得齒輪的故障信息。本文提出的包絡分析方法步驟如下: ①通過觀察齒輪箱監(jiān)測信號頻譜中的共振成分,尋找合適的解調頻帶,以利于提取由齒輪局部故障產生的脈沖信號,用于故障診斷。 ②在結構共振頻率附近(一般遠離具有較大幅值的齒輪嚙合頻率諧波分量)選擇帶通濾波器的中心頻率,通過觀察原信號的頻譜圖,選擇帶通濾波器的帶寬,使帶通頻段能覆蓋整個共振頻率區(qū)間,它能有效的去除齒輪嚙合頻率分量的影響。 ③通過對帶通濾波后的信號進行基于Hilbert變換的解調,解調后的包絡信號只包含與齒輪故障頻率相關的分量。再對包絡信號進行FFT變換獲得其頻譜圖,從而可以提取并觀察齒輪箱中齒輪的故障信息。 ④通過觀察包絡信號頻譜中不同頻率分量的特征(例如齒輪的嚙合頻率及其邊頻),可提取每個試驗齒輪包絡信號頻譜中的可相互區(qū)別的基本頻率特征,然后將這些特征作為神經網絡分類器的輸入,可用于不同損傷程度,不同類型的齒輪故障的辨別和分類。 本文針對齒輪輪齒裂紋和剝落故障進行研究,研究結果表明,通過上述方法能獲取較好的診斷結果,證明了上述方法在齒輪故障診斷中的有效性。
[Abstract]:Gear is the most widely used part in mechanical transmission system. With the development of modern science and technology, mechanical equipment becomes more and more precise, which brings difficulties to the condition monitoring of these equipment. Gear box is an important member of mechanical equipment. The monitoring of its working condition must be real-time and effective so that any malfunction in mechanical equipment can be detected and can be repaired in time at an early stage. Envelope analysis has been widely used in fault diagnosis of bearings and gears. Traditional methods require monitoring signals to be processed simultaneously in time domain before band-pass filtering in order to improve the effectiveness of envelope analysis. In practical application, it is very difficult to process the gear vibration signal in time domain synchronization and equalization, and other methods are needed to assist the envelope analysis in gear fault diagnosis. In view of the above problems, a new demodulation method is proposed in this paper. It uses the resonance frequency as the central frequency (usually far from the harmonic component of the gear meshing frequency with large amplitude) and carries on the envelope analysis to realize two types of gear vibration signal. Diagnosis and classification of local faults (crack and spalling) of gears with different degree of damage. Local faults of gears will lead to a pair of meshing gears, which will produce low-amplitude pulse signals during each rotation. It will produce modulation phenomenon with the structural resonance signal of the gear, but the low energy signal is often submerged in the high energy signal generated by other signal sources in the gear box. A method is proposed in this paper. The fault information of gear is obtained by extracting the fault excitation signal from the monitoring signal. The envelope analysis method proposed in this paper is as follows: 1 by observing the resonance components in the frequency spectrum of the gearbox monitoring signal, the suitable demodulation frequency band can be found in order to extract the pulse signal generated by the local fault of the gear for fault diagnosis. (2) the center frequency of the band-pass filter is selected near the structural resonance frequency (usually far from the harmonic component of gear meshing frequency with large amplitude), and the bandwidth of the band-pass filter is selected by observing the spectrum diagram of the original signal. The bandpass band can cover the whole resonance frequency range, which can effectively remove the influence of gear meshing frequency component. 3Demodulation based on Hilbert transform for the band-pass filtered signal. The demodulated envelope signal contains only the components related to the gear fault frequency, and then the envelope signal is transformed by FFT to obtain the spectrum diagram, so that the fault information of the gear in the gear box can be extracted and observed. (4) by observing the characteristics of different frequency components in the envelope signal spectrum (such as gear meshing frequency and its edge frequency), the basic frequency characteristics of each test gear envelope signal spectrum can be extracted. Then, these features are used as input of neural network classifier, which can be used to distinguish and classify different types of gear faults with different damage degree. In this paper, the crack and spalling fault of gear teeth are studied. The results show that the above method can obtain better diagnosis results, and proves the effectiveness of the above methods in gear fault diagnosis.
【學位授予單位】：重慶大學
【學位級別】：碩士
【學位授予年份】：2011
【分類號】：TH165.3

【共引文獻】

相關期刊論文 前1條

1 陳漢新;張琰;劉岑;;線性自適應小波理論的齒輪箱故障診斷方法[J];武漢工程大學學報;2012年12期

相關會議論文 前1條

1 陳漢新;尚云飛;賀文杰;魯艷軍;;序貫概率比檢驗在齒輪裂紋故障診斷中的應用[A];機械動力學理論及其應用[C];2011年

相關博士學位論文 前4條

1 程哲;直升機傳動系統(tǒng)行星輪系損傷建模與故障預測理論及方法研究[D];國防科學技術大學;2011年

2 孔德文;大型齒輪傳動裝置動力學及故障診斷技術研究[D];吉林大學;2008年

3 王靖;列車輪對故障振動特性及診斷關鍵技術研究[D];中南大學;2012年

4 潘巍巍;故障嚴重程度識別的有序分類特征分析方法[D];哈爾濱工業(yè)大學;2013年

相關碩士學位論文 前4條

1 車勛建;基于有序決策樹的故障程度診斷研究[D];哈爾濱工業(yè)大學;2011年

2 王龍寶;齒輪剛度計算及其有限元分析[D];江蘇大學;2007年

3 宋曉光;齒輪滾動軸承柔性轉子系統(tǒng)多故障耦合機理及辨識研究[D];華東理工大學;2013年

4 張琰;基于粒子群算法和小波理論的機械故障診斷[D];武漢工程大學;2013年

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本文編號：1407156