本文選題：滾動軸承 + 故障診斷　； 參考：《北京化工大學(xué)》2012年碩士論文

【摘要】：當(dāng)前旋轉(zhuǎn)設(shè)備正朝著高功率、智能化、一體化方向發(fā)展,有效地對機械設(shè)備進行狀態(tài)監(jiān)測,在故障早期發(fā)展階段及時發(fā)現(xiàn)并予以相應(yīng)維護,是大型旋轉(zhuǎn)機械設(shè)備運行安全性和可靠性的保證。而滾動軸承作為旋轉(zhuǎn)機械設(shè)備組的重要組成部分,因此針對于滾動軸承的狀態(tài)監(jiān)測、故障診斷和趨勢預(yù)測的研究就具有極其重要的現(xiàn)實意義。 滾動軸承故障診斷融合了機械動力學(xué)、現(xiàn)代測試技術(shù)、現(xiàn)代信號處理、數(shù)據(jù)挖掘和人工智能等多學(xué)科知識,主要包括信號預(yù)處理、特征提取、模式識別和趨勢預(yù)測四個階段,其主要任務(wù)是提取能反映設(shè)備狀態(tài)的故障特征量,判別故障類型,預(yù)測分析故障特征量的發(fā)展趨勢,根據(jù)故障嚴(yán)重程度制定適當(dāng)?shù)木S修計劃。 本文采用EMD和小波分析兩種預(yù)處理方法按照頻段或頻率將信號分解去除噪聲成分和其它干擾信息；并提出一種基于“能量-香農(nóng)熵比’的小波基選取方法,該方法將小波分解過程中的頻帶能量泄露降到最低。特征提取方面,提出了RMS序列及基于RMS序列的相對熵,其計算簡單,抑噪能力強,可有效處理故障早期或信噪比較低情況下的聲發(fā)射信號；并介紹了近似熵及其快速算法,分析闡述了計算過程中各參數(shù)對熵值和計算時間的影響。 本文利用改進粒子群優(yōu)化的神經(jīng)網(wǎng)絡(luò)對滾動軸承故障進行模式識別�；谶m應(yīng)度值對粒子群算法優(yōu)化,通過對標(biāo)準(zhǔn)速度更新公式中各參數(shù)和公式本身進行改進,并且結(jié)合其它智能算法來完善其搜索策略,突出了粒子在不同階段的全局和局部搜索能力,有效規(guī)避了搜索過程中粒子陷入局部最優(yōu)點的可能性。最后,采取基于遺傳算法的回歸預(yù)測模型對故障發(fā)展趨勢進行預(yù)測,利用遺傳算法優(yōu)化不同階次回歸模型中的系數(shù)。通過軸承外圈故障在強負(fù)載不良潤滑下的剩余壽命預(yù)測實驗對此預(yù)測算法進行試驗驗證。
[Abstract]:At present, rotating equipment is developing towards high power, intelligence and integration. It can effectively monitor the status of mechanical equipment, discover and maintain it in time in the early stage of fault development. It is the guarantee of safety and reliability of large rotating machinery. The rolling bearing is an important part of the rotating machinery, so it is of great practical significance to study the condition monitoring, fault diagnosis and trend prediction of the rolling bearing. The fault diagnosis of rolling bearing combines the knowledge of mechanical dynamics, modern test technology, modern signal processing, data mining and artificial intelligence. It includes four stages: signal preprocessing, feature extraction, pattern recognition and trend prediction. Its main task is to extract the fault characteristic quantity which can reflect the state of the equipment, to distinguish the fault type, to predict and analyze the development trend of the fault characteristic quantity, and to make the appropriate maintenance plan according to the fault severity. In this paper, two preprocessing methods, EMD and wavelet analysis, are used to decompose the signal to remove the noise components and other interference information according to the frequency band or frequency, and a wavelet basis selection method based on the "energy-Shannon entropy ratio" is proposed. This method minimizes the frequency band energy leakage in the wavelet decomposition process. In the aspect of feature extraction, the relative entropy of RMS sequence and RMS sequence is put forward, which is simple in calculation, strong in noise suppression ability, and can effectively deal with acoustic emission signal in early fault or low SNR, and the approximate entropy and its fast algorithm are also introduced. The influence of each parameter on entropy value and calculation time is analyzed. In this paper, the improved particle swarm optimization (PSO) neural network is used to identify the fault of rolling bearing. The particle swarm optimization algorithm is optimized based on fitness value. By improving the parameters of the standard speed updating formula and the formula itself, and combining with other intelligent algorithms, the search strategy is improved. It highlights the global and local searching ability of particles in different stages and effectively avoids the possibility that particles fall into local optimum in the search process. Finally, a regression prediction model based on genetic algorithm is used to predict the trend of fault development, and genetic algorithm is used to optimize the coefficients in different order regression models. The prediction algorithm is verified by the residual life prediction experiment of bearing outer ring fault under strong load and poor lubrication.
【學(xué)位授予單位】：北京化工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2012
【分類號】：TH165.3;TH133.3

【參考文獻】

相關(guān)期刊論文 前10條

1 趙美云;李力;陳保家;;滾動軸承故障聲發(fā)射信號的小波包絡(luò)譜分析[J];軸承;2008年04期

2 余光偉;鄭敏;雷子恒;宋卓遠(yuǎn);朱貿(mào);;小波變換在滾動軸承故障分析中的應(yīng)用[J];軸承;2011年07期

3 肖軍;;滾動軸承常見故障及診斷[J];工程機械與維修;2007年12期

4 高金吉;裝備系統(tǒng)故障自愈原理研究[J];中國工程科學(xué);2005年05期

5 賈艷秋;張兵;陳雪梅;;滾動軸承的故障機理及診斷[J];化工裝備技術(shù);2011年04期

6 李玉華;;最大熵譜估計在旋轉(zhuǎn)設(shè)備故障診斷中的應(yīng)用研究[J];化工自動化及儀表;2006年03期

7 徐小力,徐洪安,王少紅;旋轉(zhuǎn)機械的遺傳算法優(yōu)化神經(jīng)網(wǎng)絡(luò)預(yù)測模型[J];機械工程學(xué)報;2003年02期

8 李志農(nóng),何永勇,褚福磊;基于Wigner高階譜的機械故障診斷的研究[J];機械工程學(xué)報;2005年04期

9 李學(xué)軍;廖傳軍;褚福磊;;適于聲發(fā)射信號故障特征提取的小波函數(shù)[J];機械工程學(xué)報;2008年03期

10 呂志民,徐金梧,翟緒圣;分形維數(shù)及其在滾動軸承故障診斷中的應(yīng)用[J];機械工程學(xué)報;1999年02期

，

本文編號：2036876