本文選題：相關(guān)系數(shù) + 提升小波變換　； 參考：《西南交通大學》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,滾動軸承都是設(shè)備故障狀態(tài)的熱門研究對象。研究滾動軸承的剩余壽命有助于提高機械設(shè)備的使用壽命,提前制定合理的故障維護措施,大大的降低軸承故障給企業(yè)帶來的經(jīng)濟損失和意外傷害。本論文分別采用最小二乘支持向量機(Least Squares Support Vector Machine,以下簡稱LS-SVM)回歸預測模型進行全壽命滾動軸承的剩余壽命預測,以馬氏距離與核主成分分析融合得到的特征作為研究對象,經(jīng)過預測結(jié)果對比,核主成分分析融合的特征預測效果更好。在信號分析與處理中,需要對采集的數(shù)據(jù)進行預處理。在本論文中,我們采用改進的基于提升小波變換(Lifting Wavelet Transform,LWT)方法進行滾動軸承振動信號的降噪處理。首先,對全壽命數(shù)據(jù)進行提升小波分析得到分解后的小波系數(shù),然后對小波系數(shù)進行提升小波逆變換得到重構(gòu)之后的信號,通過計算信號的重構(gòu)分量與原信號的相關(guān)系數(shù)(Correlation Coefficient,CC),對小于設(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)進行提升小波重構(gòu)以完成消噪處理。經(jīng)過預處理的數(shù)據(jù)需要進行特征提取,研究選用時域特征、頻域特征和小波特征作為表征信號特性的參數(shù)。在模型建立之前,需要利用提取得到的信號特征構(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)的方法進行特征參數(shù)的融合,得到兩組不同的信號特征,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模型的建立和滾動軸承剩余壽命的預測。選擇徑向基函數(shù)作為模型的核函數(shù),通過參數(shù)優(yōu)化得到預測效果更好的懲罰因子與核函數(shù)參數(shù),進而得到LS-SVM的模型。論文最后利用LS-SVM模型對單參數(shù)輸入與多參數(shù)輸入的滾動軸承的剩余壽命進行預測。試驗研究結(jié)果表明,基于核主成分分析(KPCA)原理進行特征融合得到的多參數(shù)輸入的LS-SVM模型的壽命預測效果更好,精度更高,其在實際工程應用和科學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.
【學位授予單位】：西南交通大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TH133.33

【參考文獻】

相關(guān)期刊論文 前10條

1 黃天立;周浩;任偉新;陳華鵬;;基于伽馬過程的鋼橋構(gòu)件疲勞裂紋檢測維護策略優(yōu)化[J];中國公路學報;2016年05期

2 張小麗;王保建;馬猛;陳雪峰;;滾動軸承壽命預測綜述[J];機械設(shè)計與制造;2015年10期

3 沈哲輝;黃騰;唐佑輝;;灰色-馬爾科夫模型在大壩內(nèi)部變形預測中的應用[J];測繪工程;2015年02期

4 王愷;關(guān)少卿;汪令祥;王鼎奕;崔W，

本文編號：1780339