發(fā)布時(shí)間：2018-02-03 20:11

  本文關(guān)鍵詞： 齒輪故障 局部均值分解 卡爾曼濾波 支持向量機(jī) 稀疏表示　出處：《重慶三峽學(xué)院》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著齒輪在旋轉(zhuǎn)機(jī)械中所占比重的增加,齒輪的研究也顯得愈加重要。齒輪作為重要的傳動(dòng)原件之一,在處于高負(fù)荷工作狀態(tài)下,極易出現(xiàn)故障,影響生產(chǎn)的正常進(jìn)程,嚴(yán)重時(shí)會(huì)導(dǎo)致整個(gè)系統(tǒng)癱瘓,引發(fā)安全問(wèn)題和帶來(lái)巨大經(jīng)濟(jì)損失。齒輪的主要故障形式為:磨損、斷齒、點(diǎn)蝕、膠合等,當(dāng)齒輪出現(xiàn)這些故障時(shí),會(huì)引起相應(yīng)的振動(dòng)信號(hào)幅值和相位變化,產(chǎn)生幅值和相位的調(diào)制。對(duì)于振動(dòng)信號(hào)的準(zhǔn)確解調(diào)是齒輪故障研究的一個(gè)重點(diǎn)。在振動(dòng)信號(hào)處理上,局部均值分解方法可以將多分量信號(hào)分解成多個(gè)時(shí)域分量和頻域分量,將這些分量重組可以得到信號(hào)的完整時(shí)頻分布,非常適合于非線性、非平穩(wěn)信號(hào)的處理。但對(duì)于振動(dòng)信號(hào)存在強(qiáng)噪聲或信號(hào)長(zhǎng)度過(guò)大時(shí),會(huì)影響局部均值分解的計(jì)算,甚至?xí)o診斷結(jié)果帶來(lái)誤差。因此,本文在運(yùn)用局部均值分解方法進(jìn)行故障診斷的基礎(chǔ)上,結(jié)合卡爾曼濾波、支持向量機(jī)和稀疏表示方法的優(yōu)勢(shì),進(jìn)行振動(dòng)信號(hào)的降噪、故障分類和信號(hào)的壓縮,以提高齒輪故障診斷的準(zhǔn)確性、故障識(shí)別率,并縮短診斷時(shí)間。全文主要結(jié)論概況如下:(1)振動(dòng)信號(hào)中包含了大量的噪聲,容易影響故障診斷結(jié)果。結(jié)合卡爾曼濾波的局部齒輪故障診斷方法,對(duì)振動(dòng)信號(hào)進(jìn)行降噪處理,對(duì)比降噪前后的頻譜圖,可以看出降噪后故障特征數(shù)量由一個(gè)增加到了三個(gè),且特征頻率更為明顯,說(shuō)明該方法能有效減小噪聲對(duì)齒輪信號(hào)診斷的影響。(2)齒輪故障類型的識(shí)別對(duì)系統(tǒng)進(jìn)行故障診斷具有重要作用。結(jié)合局部均值分解和支持向量機(jī)的方法,對(duì)齒輪故障進(jìn)行分類判別。實(shí)驗(yàn)研究表明本文方法能對(duì)磨損故障和斷齒故障進(jìn)行有效的分類,對(duì)比經(jīng)驗(yàn)?zāi)B(tài)分解和支持向量機(jī)的齒輪故障分類法,故障識(shí)別率明顯提高,準(zhǔn)確度幾乎可達(dá)到100%。(3)采用基于匹配追蹤和局部均值的齒輪故障診斷方法,創(chuàng)建Gabor原子庫(kù),對(duì)信號(hào)進(jìn)行稀疏表示,結(jié)合譜峭度原則選取最佳分量,進(jìn)行頻譜分析,結(jié)果表明該方法能縮短信號(hào)重構(gòu)時(shí)間,加快運(yùn)行速度,準(zhǔn)確提取出故障的特征頻率。(4)采用基于正交匹配追蹤和局部均值分解的齒輪故障診斷方法,對(duì)比信號(hào)重構(gòu)前后的效果圖看出,本文方法提取的故障特征數(shù)量明顯增加,說(shuō)明在信號(hào)重構(gòu)的過(guò)程中剔除了干擾信息,明顯提高診斷的準(zhǔn)確性。
[Abstract]:With the increase of the proportion of gear in rotating machinery, the research of gear becomes more and more important. As one of the important parts of transmission, the gear is prone to malfunction under the condition of high load. Affecting the normal process of production, serious will lead to paralysis of the whole system, causing safety problems and huge economic losses. The main failure forms of gears are: wear, tooth breakage, pitting, gluing and so on. When the gear has these faults, it will cause the corresponding vibration signal amplitude and phase change. The modulation of amplitude and phase is produced. The accurate demodulation of vibration signal is an important point in the research of gear fault. The local mean decomposition method can decompose the multi-component signal into multiple time-domain components and frequency-domain components. The complete time-frequency distribution of the signal can be obtained by recombination of these components, which is very suitable for nonlinear. But when the vibration signal has strong noise or the signal length is too large, it will affect the calculation of local mean decomposition and even bring error to the diagnosis result. In this paper, based on the local mean decomposition method for fault diagnosis, combined with the advantages of Kalman filter, support vector machine and sparse representation method, the vibration signal noise reduction, fault classification and signal compression are carried out. In order to improve the accuracy of gear fault diagnosis, fault recognition rate, and shorten the diagnosis time. The main conclusions of the paper are as follows: 1) the vibration signal contains a lot of noise. It is easy to affect the result of fault diagnosis. Combined with the Kalman filter method of local gear fault diagnosis, the vibration signal is de-noised, and the spectrum before and after noise reduction is compared. It can be seen that the number of fault features increases from one to three after noise reduction, and the feature frequency is more obvious. It shows that this method can effectively reduce the influence of noise on gear signal diagnosis. (2) the identification of gear fault type plays an important role in fault diagnosis of the system. The method of local mean decomposition and support vector machine is combined. The experimental results show that this method can effectively classify the wear fault and the broken gear fault, and compare the empirical mode decomposition with the support vector machine. The fault recognition rate is improved obviously, and the accuracy can almost reach 100%. The gear fault diagnosis method based on matching tracing and local mean value is adopted to create Gabor atomic library to represent the signal sparsely. According to the principle of spectral kurtosis, the optimal component is selected and the spectrum analysis is carried out. The results show that this method can shorten the time of signal reconstruction and speed up the operation. The characteristic frequency of fault is extracted accurately. (4) the gear fault diagnosis method based on orthogonal matching tracing and local mean decomposition is adopted, and the results before and after signal reconstruction are compared. The number of fault features extracted by this method is obviously increased, which shows that the interference information is eliminated in the process of signal reconstruction, and the accuracy of diagnosis is improved obviously.
【學(xué)位授予單位】：重慶三峽學(xué)院
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TH132.41

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