本文選題：相關(guān)系數(shù) + 提升小波變換�。� 參考：《西南交通大學(xué)》2017年碩士論文

【摘要】：軸承是工業(yè)設(shè)備的重要連接部件,一直以來(lái),滾動(dòng)軸承都是設(shè)備故障狀態(tài)的熱門(mén)研究對(duì)象。研究滾動(dòng)軸承的剩余壽命有助于提高機(jī)械設(shè)備的使用壽命,提前制定合理的故障維護(hù)措施,大大的降低軸承故障給企業(yè)帶來(lái)的經(jīng)濟(jì)損失和意外傷害。本論文分別采用最小二乘支持向量機(jī)(Least Squares Support Vector Machine,以下簡(jiǎn)稱(chēng)LS-SVM)回歸預(yù)測(cè)模型進(jìn)行全壽命滾動(dòng)軸承的剩余壽命預(yù)測(cè),以馬氏距離與核主成分分析融合得到的特征作為研究對(duì)象,經(jīng)過(guò)預(yù)測(cè)結(jié)果對(duì)比,核主成分分析融合的特征預(yù)測(cè)效果更好。在信號(hào)分析與處理中,需要對(duì)采集的數(shù)據(jù)進(jìn)行預(yù)處理。在本論文中,我們采用改進(jìn)的基于提升小波變換(Lifting Wavelet Transform,LWT)方法進(jìn)行滾動(dòng)軸承振動(dòng)信號(hào)的降噪處理。首先,對(duì)全壽命數(shù)據(jù)進(jìn)行提升小波分析得到分解后的小波系數(shù),然后對(duì)小波系數(shù)進(jìn)行提升小波逆變換得到重構(gòu)之后的信號(hào),通過(guò)計(jì)算信號(hào)的重構(gòu)分量與原信號(hào)的相關(guān)系數(shù)(Correlation Coefficient,CC),對(duì)小于設(shè)定閾值的小波系數(shù)置零,最后再使用處理后的小波系數(shù)進(jìn)行提升小波重構(gòu)以完成消噪處理。經(jīng)過(guò)預(yù)處理的數(shù)據(jù)需要進(jìn)行特征提取,研究選用時(shí)域特征、頻域特征和小波特征作為表征信號(hào)特性的參數(shù)。在模型建立之前,需要利用提取得到的信號(hào)特征構(gòu)造模型的輸入特征參數(shù)。第四章使用馬氏距離(Mahalanobis Distance,MD)與核主成分分析(Kernel Principal Component Analysis,KPCA)的方法進(jìn)行特征參數(shù)的融合,得到兩組不同的信號(hào)特征,即分別為單參數(shù)特征和多參數(shù)特征。第五章主要是研究LS-SVM模型的建立和滾動(dòng)軸承剩余壽命的預(yù)測(cè)。選擇徑向基函數(shù)作為模型的核函數(shù),通過(guò)參數(shù)優(yōu)化得到預(yù)測(cè)效果更好的懲罰因子與核函數(shù)參數(shù),進(jìn)而得到LS-SVM的模型。論文最后利用LS-SVM模型對(duì)單參數(shù)輸入與多參數(shù)輸入的滾動(dòng)軸承的剩余壽命進(jìn)行預(yù)測(cè)。試驗(yàn)研究結(jié)果表明,基于核主成分分析(KPCA)原理進(jìn)行特征融合得到的多參數(shù)輸入的LS-SVM模型的壽命預(yù)測(cè)效果更好,精度更高,其在實(shí)際工程應(yīng)用和科學(xué)研究中具有更重大的意義。
[Abstract]:Bearing is an important connecting part of industrial equipment. Rolling bearing is the hot research object of equipment fault state all the time. The study of the residual life of rolling bearing is helpful to improve the service life of machinery and equipment, make reasonable maintenance measures in advance, and greatly reduce the economic loss and accidental injury caused by bearing failure. In this paper, the least squares support vector machine (LS-SVM) regression model is used to predict the residual life of rolling bearings. The features obtained from the fusion of Markov distance and kernel principal component analysis (KPCA) are taken as the research objects. By comparing the prediction results, the feature prediction effect of kernel principal component analysis fusion is better. In signal analysis and processing, the collected data need to be preprocessed. In this paper, an improved lifting Wavelet transform method based on lifting wavelet transform is used to reduce the noise of rolling bearing vibration signal. Firstly, the decomposed wavelet coefficients are obtained by lifting wavelet analysis to the whole life data, and then the reconstructed signals are obtained by lifting wavelet inverse transform of wavelet coefficients. By calculating the correlation coefficient of the reconstructed component of the signal and the correlation coefficient of the original signal, the wavelet coefficients less than the set threshold are set to zero. Finally, the wavelet coefficients after processing are reconstructed by lifting the wavelet coefficients to complete the denoising process. The preprocessed data need to be extracted by feature extraction. The time domain feature, frequency domain feature and wavelet feature are selected as the parameters to characterize the signal characteristics. Before the model is established, the input feature parameters of the model need to be constructed by using the extracted signal features. In chapter 4, the method of Mahalanobis distance MD) and kernel principal component analysis (Kernel Principal Component Analysis) are used to fuse the feature parameters, and two sets of different signal features are obtained, that is, single parameter feature and multi-parameter feature respectively. The fifth chapter mainly studies the establishment of LS-SVM model and the prediction of the remaining life of rolling bearing. The radial basis function is chosen as the kernel function of the model, and the penalty factor and kernel function parameter with better prediction effect are obtained by parameter optimization, and then the model of LS-SVM is obtained. Finally, LS-SVM model is used to predict the residual life of rolling bearing with single parameter input and multi parameter input. The experimental results show that the multi-parameter input LS-SVM model based on the kernel principal component analysis (KPA) principle has better prediction effect and higher precision, and it has more significance in practical engineering application and scientific research.
【學(xué)位授予單位】：西南交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TH133.33

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本文編號(hào)：1780339