本文關(guān)鍵詞：基于包絡(luò)分析的齒輪故障監(jiān)測及基于人工神經(jīng)網(wǎng)絡(luò)的齒輪故障分類　出處：《重慶大學(xué)》2011年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 包絡(luò)分析 解調(diào) 齒輪故障診斷 BP神經(jīng)網(wǎng)絡(luò) 齒輪故障分類

【摘要】：齒輪是機(jī)械傳動(dòng)系統(tǒng)中應(yīng)用最為廣泛的部件,現(xiàn)代科技的發(fā)展使機(jī)械設(shè)備越來越精密化,給這些設(shè)備的狀態(tài)監(jiān)測帶來困難。齒輪箱是機(jī)械設(shè)備中的重要一員,其工作狀態(tài)的監(jiān)測必須具有實(shí)時(shí)性和有效性,這樣機(jī)械設(shè)備中任何故障都能被檢測到,并能在早期得到及時(shí)的維修。 包絡(luò)分析在軸承和齒輪的故障診斷中已經(jīng)得到廣泛的應(yīng)用,傳統(tǒng)方法要求監(jiān)測信號(hào)在帶通濾波前需進(jìn)行時(shí)域同步平均處理,以提高包絡(luò)分析的效果。然而,在實(shí)際應(yīng)用中,齒輪振動(dòng)信號(hào)的時(shí)域同步平局處理很難進(jìn)行,需要其他的方法來輔助齒輪故障診斷中包絡(luò)分析。針對(duì)上述問題,本文提出了一種新的解調(diào)方法,它對(duì)齒輪振動(dòng)信號(hào)進(jìn)行以共振頻率為中心頻率(一般遠(yuǎn)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(jìn)行包絡(luò)分析,實(shí)現(xiàn)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(dǎo)致一對(duì)嚙合的齒輪,每旋轉(zhuǎn)一圈的過程中會(huì)產(chǎn)生低幅脈沖信號(hào),它會(huì)和齒輪的結(jié)構(gòu)共振信號(hào)產(chǎn)生調(diào)制現(xiàn)象。但是這個(gè)低能量的信號(hào)往往會(huì)被淹沒在齒輪箱其他信號(hào)源產(chǎn)生的高能量信號(hào)中。本文提出了一個(gè)方法,它通過從監(jiān)測信號(hào)中提取故障激勵(lì)信號(hào)從而獲得齒輪的故障信息。本文提出的包絡(luò)分析方法步驟如下: ①通過觀察齒輪箱監(jiān)測信號(hào)頻譜中的共振成分,尋找合適的解調(diào)頻帶,以利于提取由齒輪局部故障產(chǎn)生的脈沖信號(hào),用于故障診斷。 ②在結(jié)構(gòu)共振頻率附近(一般遠(yuǎn)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(hào)的頻譜圖,選擇帶通濾波器的帶寬,使帶通頻段能覆蓋整個(gè)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(duì)帶通濾波后的信號(hào)進(jìn)行基于Hilbert變換的解調(diào),解調(diào)后的包絡(luò)信號(hào)只包含與齒輪故障頻率相關(guān)的分量。再對(duì)包絡(luò)信號(hào)進(jìn)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(luò)信號(hào)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(gè)試驗(yàn)齒輪包絡(luò)信號(hào)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(jīng)網(wǎng)絡(luò)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(duì)齒輪輪齒裂紋和剝落故障進(jìn)行研究,研究結(jié)果表明,通過上述方法能獲取較好的診斷結(jié)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(xué)位授予單位】：重慶大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2011
【分類號(hào)】：TH165.3

【共引文獻(xiàn)】

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相關(guān)博士學(xué)位論文 前4條

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相關(guān)碩士學(xué)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(xué);2011年

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本文編號(hào)：1407156