本文選題：齒輪箱 + 復(fù)合故障�。� 參考：《湖南大學(xué)》2013年博士論文

【摘要】：齒輪箱是機(jī)械設(shè)備中必不可少的動(dòng)力傳輸部件,其運(yùn)行狀態(tài)將直接影響到整個(gè)機(jī)械設(shè)備能否正常工作,因此,研究齒輪箱故障診斷技術(shù)對(duì)保障機(jī)械設(shè)備的正常運(yùn)行具有重要意義。采用各種信號(hào)處理方法從齒輪箱振動(dòng)信號(hào)中提取故障特征信息是齒輪箱故障診斷的關(guān)鍵。 大量工程實(shí)踐表明,機(jī)械設(shè)備中的故障通常不止一處,往往表現(xiàn)為復(fù)合故障。不同部位、不同形式、不同程度的復(fù)合故障會(huì)對(duì)機(jī)械設(shè)備產(chǎn)生不同的影響,且各故障成分相互影響、彼此干擾,特別是在轉(zhuǎn)速變化情況下,故障特征相互重疊,給機(jī)械設(shè)備故障的全面診斷帶來(lái)了挑戰(zhàn),因此機(jī)械設(shè)備的復(fù)合故障診斷是當(dāng)前故障診斷的難點(diǎn)。針對(duì)上述問(wèn)題,本文在國(guó)家自然科學(xué)基金項(xiàng)目(項(xiàng)目編號(hào)：51275161)和湖南省科技計(jì)劃(項(xiàng)目編號(hào)：2012SK3184)的資助下,以齒輪箱為研究對(duì)象,以現(xiàn)代信號(hào)處理方法為研究手段,以復(fù)合故障診斷為研究目標(biāo),重點(diǎn)對(duì)變轉(zhuǎn)速齒輪箱復(fù)合故障振動(dòng)信號(hào)中的故障特征成分分離和故障特征提取進(jìn)行了深入系統(tǒng)地研究。 論文的主要研究工作和創(chuàng)新性成果有 (1)在分析齒輪箱中各零部件失效比重的基礎(chǔ)上,對(duì)其主要失效部件一齒輪和滾動(dòng)軸承的失效形式、失效原因、失效表現(xiàn)及振動(dòng)機(jī)理進(jìn)行了分析,并建立了齒輪和滾動(dòng)軸承的局部故障振動(dòng)信號(hào)模型。研究表明,當(dāng)齒輪出現(xiàn)局部故障時(shí),其振動(dòng)信號(hào)中會(huì)產(chǎn)生調(diào)幅調(diào)頻成分,而當(dāng)滾動(dòng)軸承出現(xiàn)局部故障時(shí),其振動(dòng)信號(hào)中會(huì)產(chǎn)生周期性的振蕩衰減沖擊成分。 (2)針對(duì)齒輪箱復(fù)合故障振動(dòng)信號(hào)中齒輪故障成分和軸承故障成分的分離和故障調(diào)制信息的提取問(wèn)題,提出了基于形態(tài)分量分析(Morphological component analysis, MCA)與能量算子解調(diào)的齒輪箱復(fù)合故障診斷方法。該方法先用MCA方法分離齒輪箱復(fù)合故障振動(dòng)信號(hào)中的齒輪故障成分和軸承故障成分；再對(duì)分離后的齒輪故障成分和軸承故障成分進(jìn)行能量算子解調(diào)分析,以提取兩成分中的故障調(diào)制信息。利用該方法對(duì)包含齒輪和滾動(dòng)軸承局部故障的齒輪箱復(fù)合故障振動(dòng)信號(hào)進(jìn)行了算法仿真和應(yīng)用實(shí)例分析,分析結(jié)果表明,對(duì)齒輪箱復(fù)合故障振動(dòng)信號(hào)中的各故障成分進(jìn)行分離后,再進(jìn)行能量算子解調(diào)分析,可有效凸顯各故障特征。 (3)針對(duì)變轉(zhuǎn)速齒輪箱復(fù)合故障振動(dòng)信號(hào)中的故障特征提取與分離問(wèn)題,提出了基于MCA與階次跟蹤的變轉(zhuǎn)速齒輪箱復(fù)合故障診斷方法。該方法先用MCA方法分離變轉(zhuǎn)速齒輪箱復(fù)合故障振動(dòng)信號(hào)中的各故障成分；再對(duì)分離后的各故障成分進(jìn)行等角度重采樣,將其轉(zhuǎn)變?yōu)榻怯蛐盘?hào)；最后對(duì)重采樣后的各故障成分進(jìn)行Hilbert包絡(luò)解調(diào)分析,以提取各故障調(diào)制信息。 通過(guò)算法仿真和應(yīng)用實(shí)例對(duì)變轉(zhuǎn)速下的齒輪局部故障和滾動(dòng)軸承局部故障進(jìn)行了分析,結(jié)果表明,該方法可有效分離變轉(zhuǎn)速下的齒輪和滾動(dòng)軸承故障特征。 (4)針對(duì)循環(huán)平穩(wěn)解調(diào)方法不適合提取變轉(zhuǎn)速齒輪箱復(fù)合故障振動(dòng)信號(hào)中故障調(diào)制信息的問(wèn)題,提出了基于線調(diào)頻小波路徑追蹤(Chirplet path pursuit, CPP)與循環(huán)平穩(wěn)解調(diào)的齒輪箱復(fù)合故障診斷方法。該方法先用CPP方法自適應(yīng)地從變轉(zhuǎn)速齒輪箱復(fù)合故障振動(dòng)信號(hào)中估計(jì)出轉(zhuǎn)速信息；再依據(jù)該轉(zhuǎn)速信息對(duì)信號(hào)進(jìn)行等角度重采樣；最后對(duì)重采樣后的角域信號(hào)進(jìn)行循環(huán)平穩(wěn)解調(diào)分析,以提取信號(hào)中的故障調(diào)制信息。利用該方法對(duì)變轉(zhuǎn)速下的齒輪箱復(fù)合故障振動(dòng)信號(hào)進(jìn)行了算法仿真和應(yīng)用實(shí)例分析,結(jié)果表明,該方法可在無(wú)轉(zhuǎn)速計(jì)的情況下有效提取變轉(zhuǎn)速齒輪箱復(fù)合故障振動(dòng)信號(hào)中的故障調(diào)制信息。 (5)在轉(zhuǎn)速大范圍變化情況下,用EEMD方法分析齒輪箱振動(dòng)信號(hào)會(huì)產(chǎn)生模態(tài)混淆。針對(duì)這一問(wèn)題,提出了基于CPP與EEMD的齒輪箱復(fù)合故障診斷方法,并將其應(yīng)用于變轉(zhuǎn)速下的齒輪箱復(fù)合故障診斷中。該方法先用CPP方法從變轉(zhuǎn)速齒輪箱復(fù)合故障振動(dòng)信號(hào)中提取轉(zhuǎn)速信息；然后依據(jù)該轉(zhuǎn)速信息對(duì)變轉(zhuǎn)速齒輪箱復(fù)合故障振動(dòng)信號(hào)進(jìn)行等角度重采樣,獲取其角域信號(hào)；再對(duì)角域信號(hào)進(jìn)行EEMD分解,獲取各IMF分量,并根據(jù)各IMF分量與角域信號(hào)的相關(guān)系數(shù)選取包含故障信息的IMF分量；最后對(duì)選取的IMF分量進(jìn)行Hilbert包絡(luò)解調(diào)分析,以提取各故障調(diào)制信息。算法仿真和應(yīng)用實(shí)例表明,該方法可有效地提取變轉(zhuǎn)速齒輪箱復(fù)合故障振動(dòng)信號(hào)中的故障特征。 機(jī)械設(shè)備的復(fù)合故障診斷是目前機(jī)械故障診斷領(lǐng)域的一大難點(diǎn)。本文以齒輪箱為研究對(duì)象,對(duì)其恒定轉(zhuǎn)速和變轉(zhuǎn)速下的齒輪和滾動(dòng)軸承復(fù)合故障振動(dòng)信號(hào)進(jìn)行分析。算法仿真和應(yīng)用實(shí)例表明,將MCA、能量算子解調(diào)、CPP、階次跟蹤、循環(huán)平穩(wěn)解調(diào)等方法相結(jié)合,可彌補(bǔ)單一信號(hào)分析方法在診斷復(fù)合故障時(shí)的不足,以實(shí)現(xiàn)優(yōu)勢(shì)互補(bǔ),具有良好的應(yīng)用前景。
[Abstract]:Gear box is an essential power transmission component in mechanical equipment . Its operation state will directly affect the normal operation of the whole mechanical equipment . Therefore , it is very important to study the gear box fault diagnosis technology to guarantee the normal operation of mechanical equipment .

A large number of engineering practice shows that the fault in mechanical equipment is usually more than one place and often appears as a composite fault . Different parts , different forms and different degrees of composite faults have different effects on the mechanical equipment , and the fault features overlap each other . In particular , under the condition of rotating speed , the fault features overlap each other . Therefore , the complex fault diagnosis of the mechanical equipment is the research object , and the fault feature component separation and fault feature extraction in the composite fault vibration signal of the variable speed gearbox are studied systematically .

The main research work and innovative results of the thesis are as follows :

( 1 ) On the basis of analyzing the failure proportion of each component in the gear box , the failure mode , the failure reason , the failure performance and the vibration mechanism of the gear and the rolling bearing of the main failure part are analyzed , and the local fault vibration signal model of the gear and the rolling bearing is established .

( 2 ) Aiming at the problem of the separation of gear fault components and the fault modulation information in the composite fault vibration signal of the gear box , a complex fault diagnosis method of gear box based on morphological component analysis ( MCA ) and energy operator demodulation is proposed .
In this paper , the fault component and the fault component of the bearing are demodulated and analyzed to extract the fault modulation information in the two components . By using the method , the complex fault vibration signal of the gear box containing the local fault of the gear and the rolling bearing is simulated and analyzed . The results show that after the fault components in the composite fault vibration signal of the gear box are separated , the energy operator demodulation analysis can be carried out , and the fault characteristics can be effectively highlighted .

( 3 ) Aiming at fault feature extraction and separation in complex fault vibration signal of variable speed gear box , a complex fault diagnosis method of variable speed gear box based on MCA and order tracking is put forward .
carrying out equal - angle resampling on the separated fault components , and converting the fault components into angular domain signals ;
and finally , Hilbert envelope demodulation analysis is carried out on each fault component after re - sampling so as to extract the fault modulation information .

The local faults of gears and the local faults of the rolling bearing are analyzed by the algorithm simulation and the application example . The results show that the method can effectively separate the fault features of gears and rolling bearings under the variable rotation speed .

( 4 ) A complex fault diagnosis method based on line frequency modulation wavelet path tracking ( CPP ) and cyclostationary demodulation is proposed .
re - sampling the signal according to the rotation speed information ;
The method can effectively extract the fault - modulated information in the complex fault vibration signal of the variable - speed gear box under the condition of no tachometer .

( 5 ) In the case of a large rotating speed range , the modal confusion can be generated by using the EEMD method to analyze the vibration signal of the gear box . According to this problem , a gear box composite fault diagnosis method based on CPP and EEMD is proposed , and the method is applied to the complex fault diagnosis of gear box under variable rotation speed .
then carrying out equal angle resampling on the composite fault vibration signal of the variable rotating speed gearbox according to the rotating speed information , and obtaining the angular domain signal thereof ;
performing EEMD decomposition on the re - diagonal domain signals to obtain each IMF component , and selecting the IMF component containing fault information according to the correlation coefficient of each IMF component and the angular domain signal ;
Finally , Hilbert envelope demodulation analysis is carried out on the selected IMF component to extract fault modulation information . The algorithm simulation and application examples show that the method can effectively extract fault features in the composite fault vibration signal of the variable speed gearbox .

The composite fault diagnosis of mechanical equipment is one of the most difficult problems in the field of mechanical fault diagnosis .

【學(xué)位授予單位】：湖南大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2013
【分類號(hào)】：TH165.3

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