本文選題：滾動(dòng)軸承 + 故障診斷��； 參考：《北京交通大學(xué)》2017年碩士論文

【摘要】：滾動(dòng)軸承被廣泛應(yīng)用于機(jī)械設(shè)備中,是旋轉(zhuǎn)設(shè)備的重要部件,同時(shí)又是一個(gè)主要的故障源,其工作狀態(tài)正常與否直接影響到機(jī)械設(shè)備的運(yùn)行穩(wěn)定性和安全性。因此,為了及時(shí)發(fā)現(xiàn)故障,降低經(jīng)濟(jì)損失,對(duì)滾動(dòng)軸承進(jìn)行運(yùn)行狀態(tài)監(jiān)測(cè)和故障診斷具有十分重要的意義。本文在對(duì)滾動(dòng)軸承結(jié)構(gòu)和振動(dòng)特性深入研究的基礎(chǔ)上,研究了共振稀疏分解方法,并針對(duì)其在參數(shù)選擇問(wèn)題,提出了優(yōu)化方法,取得了較好的信號(hào)分解結(jié)果。另外,對(duì)模式識(shí)別方法進(jìn)行了研究并提出了優(yōu)化方法,能夠?qū)L動(dòng)軸承故障信號(hào)進(jìn)行有效地模式識(shí)別。本文主要內(nèi)容如下:闡述了滾動(dòng)軸承故障診斷的研究背景和意義,總結(jié)了故障診斷技術(shù)的發(fā)展過(guò)程,系統(tǒng)介紹了滾動(dòng)軸承故障特征提取方法和模式識(shí)別方法的研究狀況。研究了滾動(dòng)軸承的故障形式和故障診斷方法,根據(jù)滾動(dòng)軸承的結(jié)構(gòu)和振動(dòng)機(jī)理,給出了故障特征頻率的計(jì)算公式,并總結(jié)了基于振動(dòng)信號(hào)的滾動(dòng)軸承故障診斷的基本步驟。深入研究了共振稀疏分解方法的基本原理,針對(duì)其參數(shù)選擇問(wèn)題,提出采用PSO算法對(duì)品質(zhì)因子的確定過(guò)程進(jìn)行優(yōu)化。為加強(qiáng)全局尋優(yōu)能力,引入了模擬退火算法和調(diào)整慣性權(quán)重因子的方法,對(duì)PSO算法作出了改進(jìn),得到了基于改進(jìn)PSO算法優(yōu)化的共振稀疏分解方法。采用不同方法對(duì)模擬信號(hào)進(jìn)行分解和頻譜分析,得到故障特征頻率,通過(guò)分解結(jié)果的對(duì)比,驗(yàn)證了本文所提方法的有效性。研究了支持向量機(jī)的分類原理,針對(duì)支持向量機(jī)在處理大樣本問(wèn)題上的局限性,提出最小二乘支持向量機(jī)分類方法,利用改進(jìn)的PSO算法對(duì)其進(jìn)行參數(shù)優(yōu)化。利用優(yōu)化的分類方法對(duì)Wine數(shù)據(jù)進(jìn)行分類識(shí)別,證明了優(yōu)化的分類方法的有效性。利用滾動(dòng)軸承的故障振動(dòng)信號(hào),對(duì)本文提出的故障特征提取方法和模式識(shí)別方法進(jìn)行了實(shí)驗(yàn)驗(yàn)證。對(duì)滾動(dòng)軸承故障信號(hào)進(jìn)行共振稀疏分解,一方面,對(duì)分解得到的低共振分量進(jìn)行頻譜分析,提取出故障特征頻率;另一方面,將低共振分量對(duì)應(yīng)的系數(shù)作為支持向量機(jī)的輸入,進(jìn)行故障模式識(shí)別。利用不同方法對(duì)滾動(dòng)軸承信號(hào)進(jìn)行分解,通過(guò)故障特征頻率提取結(jié)果和模式識(shí)別分類準(zhǔn)確率的對(duì)比,表明了本文所提優(yōu)化方法的優(yōu)越性和魯棒性。
[Abstract]:Rolling bearing is widely used in mechanical equipment. It is an important part of rotating equipment and also a main fault source. Whether its working condition is normal or not has a direct impact on the operation stability and safety of mechanical equipment. Therefore, in order to find fault in time and reduce economic loss, it is very important to monitor and diagnose rolling bearing running condition. Based on the deep study of the structure and vibration characteristics of rolling bearings, the resonance sparse decomposition method is studied in this paper, and an optimization method is proposed to solve the problem of parameter selection, and a better signal decomposition result is obtained. In addition, the method of pattern recognition is studied and the optimization method is put forward, which can effectively recognize the fault signals of rolling bearings. The main contents of this paper are as follows: the research background and significance of rolling bearing fault diagnosis are expounded, the development process of fault diagnosis technology is summarized, and the research status of fault feature extraction method and pattern recognition method of rolling bearing is systematically introduced. The fault form and fault diagnosis method of rolling bearing are studied. According to the structure and vibration mechanism of rolling bearing, the calculation formula of fault characteristic frequency is given, and the basic steps of fault diagnosis of rolling bearing based on vibration signal are summarized. In this paper, the basic principle of the resonance sparse decomposition method is deeply studied, and the PSO algorithm is proposed to optimize the process of determining the quality factor in view of the problem of parameter selection. In order to enhance the ability of global optimization, the simulated annealing algorithm and the method of adjusting inertia weight factor are introduced. The PSO algorithm is improved, and the resonance sparse decomposition method based on the improved PSO algorithm is obtained. Different methods are used to decompose the analog signal and the frequency spectrum is analyzed, and the fault characteristic frequency is obtained. The validity of the proposed method is verified by the comparison of the decomposition results. In this paper, the classification principle of support vector machine is studied. Aiming at the limitation of support vector machine in dealing with large sample problem, the least square support vector machine classification method is proposed, and the improved PSO algorithm is used to optimize its parameters. The optimized classification method is used to classify and identify Wine data, which proves the effectiveness of the optimized classification method. The fault feature extraction method and pattern recognition method proposed in this paper are verified by using the fault vibration signal of rolling bearing. The resonance sparse decomposition of rolling bearing fault signal is carried out. On the one hand, the frequency spectrum of the low resonance component is analyzed to extract the fault characteristic frequency; on the other hand, the coefficient corresponding to the low resonance component is used as the input of the support vector machine. Fault pattern recognition is carried out. Different methods are used to decompose the rolling bearing signals. The comparison of fault feature frequency extraction results with the classification accuracy of pattern recognition shows the superiority and robustness of the optimization method proposed in this paper.
【學(xué)位授予單位】：北京交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TH133.33

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 余發(fā)軍;周鳳星;嚴(yán)�？�;;基于字典學(xué)習(xí)的軸承早期故障稀疏特征提取[J];振動(dòng)與沖擊;2016年06期

2 馮毅;曹勁然;陸寶春;張登峰;;基于連續(xù)峭度優(yōu)化的滾動(dòng)軸承故障特征提取小波變換方法[J];振動(dòng)與沖擊;2015年14期

3 王洪濤;鄭乃清;劉輝軍;;基于共振稀疏分解的局部放電信號(hào)窄帶干擾抑制新方法[J];工礦自動(dòng)化;2015年05期

4 羅毅;甄立敬;;基于小波包與倒頻譜分析的風(fēng)電機(jī)組齒輪箱齒輪裂紋診斷方法[J];振動(dòng)與沖擊;2015年03期

5 徐健;張語(yǔ)R，

本文編號(hào)：2092406