本文選題：分?jǐn)?shù)階傅里葉變換 + 高速列車監(jiān)測(cè)數(shù)據(jù)��； 參考：《西南交通大學(xué)》2013年碩士論文

【摘要】：隨著我國(guó)高鐵技術(shù)的飛速發(fā)展,列車車輛的速度也在不斷提高,而在列車速度提高的同時(shí)也惡化了列車的動(dòng)態(tài)環(huán)境,使得列車的輪軌作用力增加、各個(gè)部件的振動(dòng)和蛇行運(yùn)動(dòng)加劇。同時(shí)隨著列車服役時(shí)間的增加,列車零部件磨損加快導(dǎo)致其性能參數(shù)快速蛻變,嚴(yán)重影響了列車的運(yùn)行品質(zhì)。因此如何提取高速列車監(jiān)測(cè)數(shù)據(jù)的有效特征,并快速準(zhǔn)確的估計(jì)出高速列車安全服役性能,已成為目前高速列車安全預(yù)警領(lǐng)域亟待解決的問題。 高速列車監(jiān)測(cè)數(shù)據(jù)具有非線性和非平穩(wěn)性,分?jǐn)?shù)階傅里葉變換(Fractional Fourier Transform, FRFT)是傅里葉變換的一種推廣形式,能夠同時(shí)表征信號(hào)的時(shí)域和頻域信息,在非平穩(wěn)信號(hào)處理領(lǐng)域得到廣泛的應(yīng)用。因此本文采用分?jǐn)?shù)階傅里葉變換提取信號(hào)的分?jǐn)?shù)域特征,并運(yùn)用分?jǐn)?shù)域特征實(shí)現(xiàn)列車運(yùn)行狀態(tài)的評(píng)估,主要研究工作如下： 分析了短時(shí)傅里葉變換、小波變換、魏格納-威利分布(WVD)和Randon-Wigner分布等經(jīng)典時(shí)頻分析方法的優(yōu)缺點(diǎn),并深入研究了新時(shí)頻分析方法分?jǐn)?shù)階傅里葉變換的基本原理和相關(guān)理論知識(shí)。提取高速列車監(jiān)測(cè)數(shù)據(jù)的分?jǐn)?shù)域特征,首先對(duì)高速列車監(jiān)測(cè)數(shù)據(jù)進(jìn)行分?jǐn)?shù)階傅坐葉變換,將數(shù)據(jù)變換到分?jǐn)?shù)階傅里葉空間,然后對(duì)變換后的三維數(shù)據(jù)進(jìn)行側(cè)面投影,得到不同分?jǐn)?shù)階下的信號(hào)峰值曲線,最后計(jì)算峰值曲線的統(tǒng)計(jì)特征。 研究了高速列車轉(zhuǎn)向架的結(jié)構(gòu)和相關(guān)動(dòng)力學(xué)分析。運(yùn)用分?jǐn)?shù)階傅里葉變換提取高速列車監(jiān)測(cè)數(shù)據(jù)特征,研究速度對(duì)仿真數(shù)據(jù)各個(gè)工況特征分布情況的影響。對(duì)所有通道單 工況的仿真數(shù)據(jù)進(jìn)行仿真,并運(yùn)用支持向量機(jī)對(duì)四種單一工況進(jìn)行分類,通過分析不同通道不同速度下四種工況的識(shí)別率情況,統(tǒng)計(jì)出對(duì)各個(gè)工況具有較好分類效果的通道。對(duì)試驗(yàn)監(jiān)測(cè)數(shù)據(jù)進(jìn)行仿真和分類統(tǒng)計(jì),進(jìn)一步驗(yàn)證通道和特征的有效性。 通過比較兩種單一工況及其混合工況在橫向和垂向上的特征分布情況,來探討了多故障工況與單一工況之間的關(guān)聯(lián)。對(duì)參數(shù)漸變工況進(jìn)行仿真,研究列車從原車正常變化到三種完全故障過程中的特征分布情況。 綜上所述,本文運(yùn)用分?jǐn)?shù)階傅里葉變換對(duì)高速列車不同工況的數(shù)據(jù)進(jìn)行仿真分析,實(shí)驗(yàn)結(jié)果證實(shí)了分?jǐn)?shù)域特征對(duì)高速列車故障識(shí)別的可行性與有效性。
[Abstract]:With the rapid development of high-speed rail technology in China, the speed of train vehicles is also increasing, while the speed of the train has also deteriorated the dynamic environment of the train, which makes the wheel-rail force of the train increase. The vibration and snake motion of each component are intensified. At the same time, with the increase of train service time, the wear of train parts is accelerated, which leads to the rapid transformation of its performance parameters, which seriously affects the running quality of the train. Therefore, how to extract the effective features of high-speed train monitoring data and estimate the safe service performance of high-speed train quickly and accurately has become an urgent problem in the field of high-speed train safety warning. High speed train monitoring data are nonlinear and non-stationary. Fractional Fourier transform (FRFT) is a generalized form of Fourier transform, which can represent the time domain and frequency domain information of the signal at the same time. It is widely used in the field of non-stationary signal processing. Therefore, fractional Fourier transform is used to extract the fractional domain feature of the signal, and the fractional domain feature is used to evaluate the train running state. The main research work is as follows: the short time Fourier transform, wavelet transform are analyzed. The advantages and disadvantages of the classical time-frequency analysis methods, such as WVD and Randon-Wigner distribution, are discussed. The basic principle and relevant theoretical knowledge of the new time-frequency analysis method, fractional Fourier transform, are studied. The fractional domain feature of the high-speed train monitoring data is extracted. Firstly, the fractional Fourier transform is carried out on the high-speed train monitoring data, and the data is transformed into the fractional Fourier space, and then the transformed 3D data is side-projected. The peak curve of signal with different fractional order is obtained, and the statistical characteristics of the peak curve are calculated at last. The structure and dynamic analysis of high-speed train bogies are studied. The fractional-order Fourier transform is used to extract the features of high-speed train monitoring data, and the influence of speed on the distribution of simulation data under different operating conditions is studied. The simulation data of all channels are simulated, and the support vector machine is used to classify the four single working conditions, and the recognition rate of the four working conditions under different channels and different speeds is analyzed. The channel which has good classification effect for each working condition is counted out. The experimental monitoring data are simulated and classified to verify the validity of the channel and features. By comparing the horizontal and vertical characteristic distributions of two kinds of single working conditions and their mixing conditions, the relationship between the multi-fault conditions and the single working conditions is discussed. The characteristic distribution of train from normal change to three kinds of complete faults is studied by simulation of parameter gradient condition. To sum up, the fractional Fourier transform is used to simulate and analyze the data of high-speed train under different working conditions. The experimental results confirm the feasibility and effectiveness of the fractional domain feature in fault identification of high-speed train.
【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】：2013
【分類號(hào)】：U298.1

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 冉鑫,馬世偉,曹家麟;基于Radon-Wigner變換的多分量LFM信號(hào)的檢測(cè)[J];上海大學(xué)學(xué)報(bào)(自然科學(xué)版);2001年02期

2 翟婉明,蔡成標(biāo),王開云;軌道剛度對(duì)列車走行性能的影響[J];鐵道學(xué)報(bào);2000年06期

3 黃采倫;樊曉平;陳春陽(yáng);陳特放;張劍;;基于小波系數(shù)提取及離散余弦包絡(luò)分析的機(jī)車牽引齒輪故障診斷方法[J];鐵道學(xué)報(bào);2008年02期

4 陽(yáng)光武;肖守訥;張衛(wèi)華;;列車曲線上制動(dòng)時(shí)的安全性分析[J];鐵道學(xué)報(bào);2009年01期

5 翟婉明,王開云;機(jī)車車輛側(cè)向通過道岔時(shí)的運(yùn)行安全性評(píng)估[J];同濟(jì)大學(xué)學(xué)報(bào)(自然科學(xué)版);2004年03期

6 姜波;李迎盼;田日才;;小波奇異性分析在DS-UWB降噪中的應(yīng)用[J];通信技術(shù);2010年01期

7 周映虹;馬爭(zhēng)鳴;;基于上下文選擇編碼的小波圖像編碼算法[J];通信學(xué)報(bào);2008年01期

8 張秀媛;鐵路提速列車技術(shù)安全監(jiān)測(cè)系統(tǒng)評(píng)價(jià)方法研究[J];中國(guó)安全科學(xué)學(xué)報(bào);2004年06期

9 陳特放,黃采倫,樊曉平;基于小波分析的機(jī)車走行部故障診斷方法[J];中國(guó)鐵道科學(xué);2005年04期

10 丁夏完,劉葆,劉金朝,王成國(guó),Riemenscheider S D,胡曉依;基于自適應(yīng)STFT的貨車滾動(dòng)軸承故障診斷[J];中國(guó)鐵道科學(xué);2005年06期

相關(guān)博士學(xué)位論文 前5條

1 張兵;列車關(guān)鍵部件安全監(jiān)測(cè)理論與分析研究[D];西南交通大學(xué);2008年

2 楊文濤;分?jǐn)?shù)傅里葉變換在數(shù)字圖像處理中的應(yīng)用研究[D];華中科技大學(xué);2007年

3 羅震;高速鐵路無(wú)砟軌道結(jié)構(gòu)受力及輪軌動(dòng)力作用分析[D];西南交通大學(xué);2008年

4 唐s，

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