基于局部特征尺度分解的齒輪故障診斷方法研究
發(fā)布時(shí)間：2019-01-20 08:06
【摘要】：齒輪是機(jī)械設(shè)備中重要的連接和傳動部件，在機(jī)械設(shè)備運(yùn)行過程中發(fā)揮有重要的作用。但由于齒輪本身結(jié)構(gòu)的特點(diǎn)，是特別易受損害和出現(xiàn)故障的零部件，如果不及時(shí)發(fā)現(xiàn)故障，將會給整個(gè)生產(chǎn)和社會造成很大的損失�？梢婟X輪故障診斷研究的重要性。 齒輪故障診斷的關(guān)鍵是從齒輪的振動信號中提取故障特征，信號分析與處理是提取故障特征最常用的方法。然而，齒輪發(fā)生故障時(shí)，其振動信號大多是非平穩(wěn)、非線性的時(shí)變信號，這就要選擇合適的信號分析方法。本文正是針對齒輪故障振動信號的非平穩(wěn)性及其多為多分量的調(diào)制信號之和等特性，將一種新的自適應(yīng)時(shí)頻分析方法—局部特征尺度分解方法(Local characteristic-scaledecomposition，簡稱LCD)引入到齒輪故障診斷中，進(jìn)一步將該方法與倒頻譜、能量矩、雙譜等方法相結(jié)合應(yīng)用于齒輪故障診斷中，并取得了較好的分析效果。本文的主要研究內(nèi)容如下： 1、論文提出了一種新的自適應(yīng)時(shí)頻分析方法—局部特征尺度分解方法(Localcharacteristic-scale decomposition，簡稱LCD)，通過對其理論本身進(jìn)行剖析研究和對仿真信號進(jìn)行分析，指出了LCD方法有一定的優(yōu)越性。同時(shí)，LCD方法也有一定的缺陷和不足，論文對其進(jìn)行了改進(jìn)，并且改進(jìn)后的LCD方法對信號進(jìn)行分解得到的分量有更好的光滑性。 2、針對齒輪故障振動信號大多數(shù)為若干的調(diào)幅調(diào)頻信號之和這一特點(diǎn)，將基于B樣條函數(shù)的局部特征尺度分解方法(B spline-based Localcharacteristic-scale decomposition，，簡稱BLCD)和倒頻譜應(yīng)用于齒輪故障診斷中，有效的提取了故障齒輪的故障特征。 3、把基于三次樣條函數(shù)的局部特征尺度分解方法和能量矩相結(jié)合應(yīng)用于齒輪的故障診斷，通過對正常齒輪和斷齒齒輪進(jìn)行分析，驗(yàn)證了該方法的有效性。 4、把基于有理樣條函數(shù)的局部特征尺度分解方法(RLCD)和雙譜應(yīng)用于齒輪的故障診斷中，從雙譜圖中有效的提取了故障齒輪的故障特征。這也為齒輪的故障診斷提供了一種新的方法。
[Abstract]:Gear is an important connection and transmission part in mechanical equipment, which plays an important role in the operation of mechanical equipment. However, because of the characteristics of gear structure, it is especially vulnerable to damage and fault parts. If failure is not found in time, it will cause great losses to the whole production and society. This shows the importance of gear fault diagnosis. The key of gear fault diagnosis is to extract fault features from gear vibration signals. Signal analysis and processing are the most commonly used methods to extract fault features. However, when gear failure occurs, the vibration signal is mostly non-stationary and nonlinear time-varying signal, so we should choose the appropriate signal analysis method. In this paper, aiming at the nonstationarity of gear fault vibration signal and the sum of multi-component modulated signals, a new adaptive time-frequency analysis method, local eigenscale decomposition method (Local characteristic-scaledecomposition,), is proposed. LCD) is introduced into gear fault diagnosis and applied to gear fault diagnosis with the combination of cepstrum, energy moment, bispectrum and so on, and good results are obtained. The main contents of this paper are as follows: 1. A new adaptive time-frequency analysis method, called local feature scale decomposition (Localcharacteristic-scale decomposition,), is proposed in this paper. By analyzing the theory itself and the simulation signal, it is pointed out that the LCD method has some advantages. At the same time, the LCD method also has some defects and shortcomings. The paper improves it, and the improved LCD method has better smoothness to the components of signal decomposition. 2. In view of the fact that the vibration signals of gear fault are mostly the sum of amplitude modulation and frequency modulation signals, the local characteristic scale decomposition method (B spline-based Localcharacteristic-scale decomposition,) based on B-spline function is proposed. BLCD) and cepstrum are used in gear fault diagnosis, and the fault features of fault gears are extracted effectively. 3. The local characteristic scale decomposition method based on cubic spline function and the energy moment are applied to the fault diagnosis of gear. The validity of this method is verified by analyzing the normal gear and broken gear. 4. The local feature scale decomposition method based on rational spline function (RLCD) and bispectrum are applied to the fault diagnosis of gears, and the fault features of faulty gears are extracted effectively from the bispectrum. It also provides a new method for gear fault diagnosis.
【學(xué)位授予單位】：湖南大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2012
【分類號】：TH165.3
[Abstract]:Gear is an important connection and transmission part in mechanical equipment, which plays an important role in the operation of mechanical equipment. However, because of the characteristics of gear structure, it is especially vulnerable to damage and fault parts. If failure is not found in time, it will cause great losses to the whole production and society. This shows the importance of gear fault diagnosis. The key of gear fault diagnosis is to extract fault features from gear vibration signals. Signal analysis and processing are the most commonly used methods to extract fault features. However, when gear failure occurs, the vibration signal is mostly non-stationary and nonlinear time-varying signal, so we should choose the appropriate signal analysis method. In this paper, aiming at the nonstationarity of gear fault vibration signal and the sum of multi-component modulated signals, a new adaptive time-frequency analysis method, local eigenscale decomposition method (Local characteristic-scaledecomposition,), is proposed. LCD) is introduced into gear fault diagnosis and applied to gear fault diagnosis with the combination of cepstrum, energy moment, bispectrum and so on, and good results are obtained. The main contents of this paper are as follows: 1. A new adaptive time-frequency analysis method, called local feature scale decomposition (Localcharacteristic-scale decomposition,), is proposed in this paper. By analyzing the theory itself and the simulation signal, it is pointed out that the LCD method has some advantages. At the same time, the LCD method also has some defects and shortcomings. The paper improves it, and the improved LCD method has better smoothness to the components of signal decomposition. 2. In view of the fact that the vibration signals of gear fault are mostly the sum of amplitude modulation and frequency modulation signals, the local characteristic scale decomposition method (B spline-based Localcharacteristic-scale decomposition,) based on B-spline function is proposed. BLCD) and cepstrum are used in gear fault diagnosis, and the fault features of fault gears are extracted effectively. 3. The local characteristic scale decomposition method based on cubic spline function and the energy moment are applied to the fault diagnosis of gear. The validity of this method is verified by analyzing the normal gear and broken gear. 4. The local feature scale decomposition method based on rational spline function (RLCD) and bispectrum are applied to the fault diagnosis of gears, and the fault features of faulty gears are extracted effectively from the bispectrum. It also provides a new method for gear fault diagnosis.
【學(xué)位授予單位】：湖南大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2012
【分類號】：TH165.3
【參考文獻(xiàn)】
相關(guān)期刊論文 前10條
1 潘泉;孟晉麗;張磊;程詠梅;張洪才;;小波濾波方法及應(yīng)用[J];電子與信息學(xué)報(bào);2007年01期
2 胡維平;莫家玲;龔英姬;趙方偉;杜明輝;;經(jīng)驗(yàn)?zāi)B(tài)分解中多種邊界處理方法的比較研究[J];電子與信息學(xué)報(bào);2007年06期
3 熊學(xué)軍,郭炳火,胡筱敏,劉建軍;EMD方法和Hilbert譜分析法的應(yīng)用與探討[J];黃渤海海洋;2002年02期
4 姜萬錄,張淑清,王益群;液壓泵故障的小波變換診斷方法[J];機(jī)械工程學(xué)報(bào);2001年06期
5 程軍圣,于德介,楊宇;基于EMD的能量算子解調(diào)方法及其在機(jī)械故障診斷中的應(yīng)用[J];機(jī)械工程學(xué)報(bào);2004年08期
6 鄧擁軍,王偉,錢成春,王忠,戴德君;EMD方法及Hilbert變換中邊界問題的處理[J];科學(xué)通報(bào);2001年03期
7 李寶年;齒輪箱故障診斷研究[J];煤礦機(jī)械;1997年01期
8 張學(xué)工;關(guān)于統(tǒng)計(jì)學(xué)習(xí)理論與支持向量機(jī)[J];自動化學(xué)報(bào);2000年01期
9 黃大吉,趙進(jìn)平,蘇紀(jì)蘭;希爾伯特-黃變換的端點(diǎn)延拓[J];海洋學(xué)報(bào)(中文版);2003年01期
10 陳忠,鄭時(shí)雄;EMD信號分析方法邊緣效應(yīng)的分析[J];數(shù)據(jù)采集與處理;2003年01期
相關(guān)碩士學(xué)位論文 前3條
1 梁鋒;通用性齒輪箱狀態(tài)監(jiān)測與故障診斷系統(tǒng)的研究[D];重慶大學(xué);2004年
2 薛Z
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