本文選題：時(shí)頻分析 + 分?jǐn)?shù)階Fourier變換�。� 參考：《南昌航空大學(xué)》2017年碩士論文

【摘要】：本論文是在國(guó)家自然科學(xué)基金(51261024,51075372,51265039,50775208),國(guó)家重點(diǎn)研發(fā)計(jì)劃項(xiàng)目(2016YFF0203000)、機(jī)械傳動(dòng)國(guó)家重點(diǎn)實(shí)驗(yàn)室開放基金(No.SKLMT-KFKT-201514)和江西省教育廳科技計(jì)劃項(xiàng)目(No.GJJ12405,No.GJJ150699)資助下展開研究,利用分?jǐn)?shù)階Fourier變換具有的獨(dú)特特點(diǎn),將基于Fourier變換的傳統(tǒng)信號(hào)處理方法推廣到分?jǐn)?shù)階Fourier變換領(lǐng)域,提出了一些行之有效的分?jǐn)?shù)階非平穩(wěn)信號(hào)處理方法,分?jǐn)?shù)階小波變換、分?jǐn)?shù)階S變換、分?jǐn)?shù)階廣義S變換、分?jǐn)?shù)階局部均值分解等方法,并將之應(yīng)用在旋轉(zhuǎn)機(jī)器故障診斷中,取得了比較好的創(chuàng)新性成果。本文主要研究?jī)?nèi)容包括以下幾個(gè)方面:第1章,闡述了課題研究的背景和研究意義,論述了時(shí)頻分析方法的國(guó)內(nèi)外現(xiàn)狀,這里,主要從基于核函數(shù)的時(shí)頻分析、基于信號(hào)分解的時(shí)頻分析、參數(shù)化時(shí)頻分析的三方面加以論述。同時(shí),還論述了現(xiàn)有的時(shí)頻分析方法在機(jī)械故障診斷中的應(yīng)用現(xiàn)狀,在此基礎(chǔ)上,提出了本文所研究的內(nèi)容以及對(duì)現(xiàn)有內(nèi)容的創(chuàng)新之處。第2章,論述了分?jǐn)?shù)階小波變換的定義、性質(zhì)和算法,在此基礎(chǔ)上,提出了基于分?jǐn)?shù)階小波變換的機(jī)械故障診斷方法,并與傳統(tǒng)小波變換進(jìn)行了對(duì)比研究。該方法結(jié)合了小波變換和分?jǐn)?shù)階Fourier變換各自的優(yōu)點(diǎn),同時(shí)摒棄了各自的缺點(diǎn),在分析中加入了一個(gè)可調(diào)節(jié)變量即分?jǐn)?shù)階階數(shù),使得該方法對(duì)非平穩(wěn)信號(hào)的分析更加靈活。仿真研究表明,分?jǐn)?shù)階小波變換優(yōu)于傳統(tǒng)的小波變換,在對(duì)信號(hào)的濾波方面具有優(yōu)勢(shì)。最后,將提出的方法應(yīng)用到轉(zhuǎn)子碰磨故障中,實(shí)驗(yàn)結(jié)果驗(yàn)證了提出方法的有效性。第3章,論述了標(biāo)準(zhǔn)S變換的定義、算法及性質(zhì),本章將標(biāo)準(zhǔn)S變換和分?jǐn)?shù)階Fourier變換結(jié)合,構(gòu)造了分?jǐn)?shù)階S變換,給出了分?jǐn)?shù)階S變換的定義式子和算法,并與標(biāo)準(zhǔn)S變換進(jìn)行對(duì)比分析。該方法將S變換和分?jǐn)?shù)階Fourier變換各自的優(yōu)點(diǎn)進(jìn)行了結(jié)合,就能根據(jù)信號(hào)的特點(diǎn)來(lái)選擇合適的分?jǐn)?shù)階階數(shù),從而做到對(duì)信號(hào)進(jìn)行準(zhǔn)確的分析。仿真結(jié)果表明,分?jǐn)?shù)階S變換具有明顯的優(yōu)勢(shì),不僅能反映信號(hào)的頻率結(jié)構(gòu),而且得到了著比標(biāo)準(zhǔn)S變換更高的時(shí)頻分辨率。最后,將提出的方法應(yīng)用到滾動(dòng)軸承故障診斷中,實(shí)驗(yàn)結(jié)果進(jìn)一步驗(yàn)證了提出的方法的有效性。第4章,論述了廣義S變換的定義、算法及性質(zhì),類似分?jǐn)?shù)階S變換的定義,構(gòu)造了分?jǐn)?shù)階廣義S變換,給出了分?jǐn)?shù)階廣義S變換的定義和算法,并與標(biāo)準(zhǔn)S變換、廣義S變換進(jìn)行對(duì)比分析。該方法充分吸收了廣義S變換和分?jǐn)?shù)階Fourier變換各自的優(yōu)點(diǎn),并將兩者的優(yōu)點(diǎn)進(jìn)行了融合,使得分?jǐn)?shù)階廣義S變換能靈活的對(duì)非平穩(wěn)信號(hào)進(jìn)行分析。仿真結(jié)果表明,分?jǐn)?shù)階廣義S變換具有明顯的優(yōu)勢(shì),不僅能反映信號(hào)的頻率結(jié)構(gòu),而且得到了比標(biāo)準(zhǔn)S變換、廣義S變換更高的時(shí)頻分辨率。最后,將提出的方法應(yīng)用到滾動(dòng)軸承故障診斷中,實(shí)驗(yàn)結(jié)果進(jìn)一步驗(yàn)證了提出的分?jǐn)?shù)階廣義S變換的有效性。第5章,結(jié)合局部均值分解和分?jǐn)?shù)階Fourier變換的各自優(yōu)點(diǎn),構(gòu)造了分?jǐn)?shù)階局部均值分解,給出了其定義和算法,在此基礎(chǔ)上,提出了基于分?jǐn)?shù)階LMD的Wigner分布的機(jī)械故障診斷方法,同時(shí),將提出的方法與傳統(tǒng)的Wigner分布進(jìn)行了對(duì)比研究,仿真和實(shí)驗(yàn)結(jié)果表明,分?jǐn)?shù)階局部均值分解優(yōu)于傳統(tǒng)的Wigner分布,能有效地消除其交叉項(xiàng),具有很高的時(shí)頻分辨率,能有效低反映故障的特征頻率。第6章,對(duì)本文的研究?jī)?nèi)容給以了總結(jié),并給出了值得進(jìn)一步研究問(wèn)題。
[Abstract]:This paper is based on the National Natural Science Foundation (51261024510753725126503950775208), the national key research and development project (2016YFF0203000), the open fund of the National Key Laboratory of mechanical transmission (No.SKLMT-KFKT-201514) and the No.GJJ12405 (No.GJJ150699) of the Jiangxi Provincial Education Department (No.GJJ12405, No.GJJ150699), using fractional Fourie The unique characteristic of R transform is to extend the traditional signal processing method based on Fourier transform to the domain of fractional Fourier transform. Some effective fractional order nonstationary signal processing methods, fractional order wavelet transform, fractional order S transform, fractional order generalized S transform, fractional order partial mean decomposition and so on are proposed and applied. The main research contents of this paper include the following aspects: the first chapter, the following aspects: the background and the significance of the research, the status of the time frequency analysis method at home and abroad, and the time frequency analysis based on the kernel function and the time frequency analysis based on the signal decomposition, The three aspects of the parametric time-frequency analysis are discussed. At the same time, the application of the existing time-frequency analysis method in the mechanical fault diagnosis is also discussed. On this basis, the contents of this paper and the innovation of the existing content are put forward. In the second chapter, the definition, properties and algorithms of fractional small wave transform are discussed. A method of mechanical fault diagnosis based on fractional wavelet transform is proposed and compared with the traditional wavelet transform. The method combines the advantages of the wavelet transform and the fractional Fourier transform. At the same time, the method discarded their respective shortcomings and added an adjustable variable, the fractional order in the analysis, which makes the method not flat. The analysis of the stable signal is more flexible. The simulation study shows that the fractional wavelet transform is superior to the traditional wavelet transform and has advantages in the signal filtering. Finally, the proposed method is applied to the rotor rubbing fault. The experimental results verify the effectiveness of the proposed method. The third chapter discusses the definition, algorithm and properties of the standard S transform. In this chapter, the fractional order S transform is constructed by combining standard S transform with fractional order Fourier transform. The definition and algorithm of fractional order S transform are given, and the comparison analysis is made with standard S transform. This method combines the advantages of S transform and fractional Fourier transform, and can choose the appropriate fractional order according to the characteristics of the signal. The simulation results show that the fractional S transform has obvious advantages, which can not only reflect the frequency structure of the signal, but also get a higher time-frequency resolution than the standard S transform. Finally, the proposed method is applied to the fault diagnosis of the rolling bearing, and the experimental results are further verified. The fourth chapter discusses the definition, algorithm and properties of generalized S transform, which is similar to the definition of fractional order S transform, constructs fractional order generalized S transform, gives the definition and algorithm of fractional order generalized S transform, and compares it with standard S transformation and generalized S transformation. This method fully absorbs the generalized S transformation and fractional order Fou. Rier transform each advantage and combine the advantages of the two. The fractional order generalized S transform can be used to analyze the nonstationary signal flexibly. The simulation results show that the fractional order generalized S transform has obvious advantages, not only can reflect the frequency structure of the signal, but also get a higher time frequency than the standard S transform and the generalized S transform. Finally, the proposed method is applied to the fault diagnosis of rolling bearings. The experimental results further verify the effectiveness of the proposed fractional order generalized S transform. The fifth chapter, combining the advantages of local mean decomposition and fractional order Fourier transform, constructs fractional order partial mean decomposition, and gives its definition and algorithm. The method of mechanical fault diagnosis based on Wigner distribution based on fractional LMD is proposed. At the same time, the proposed method is compared with the traditional Wigner distribution. The simulation and experimental results show that the fractional partial mean decomposition is superior to the traditional Wigner distribution. It can effectively eliminate its cross term, with high time-frequency resolution and low efficiency. The sixth chapter summarizes the research contents of this paper, and gives further research questions.

【學(xué)位授予單位】：南昌航空大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TH17

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 唐貴基;龐彬;;基于改進(jìn)的希爾伯特振動(dòng)分解的機(jī)械故障診斷方法研究[J];振動(dòng)與沖擊;2015年03期

2 郭遠(yuǎn)晶;魏燕定;周曉軍;傅雷;;S變換時(shí)頻譜SVD降噪的沖擊特征提取方法[J];振動(dòng)工程學(xué)報(bào);2014年04期

3 朱可恒;宋希庚;薛冬新;;希爾伯特振動(dòng)分解在滾動(dòng)軸承故障診斷中應(yīng)用[J];振動(dòng)與沖擊;2014年14期

4 張淑清;孫國(guó)秀;李亮;李新新;監(jiān)雄;;基于LMD近似熵和FCM聚類的機(jī)械故障診斷研究[J];儀器儀表學(xué)報(bào);2013年03期

5 胥永剛;陸明;謝志聰;;基于固有時(shí)間尺度分解的能量算子解調(diào)法及故障診斷應(yīng)用[J];海軍工程大學(xué)學(xué)報(bào);2013年01期

6 沈長(zhǎng)青;謝偉達(dá);朱忠奎;劉方;黃偉國(guó);孔凡讓;;基于EEMD和改進(jìn)的形態(tài)濾波方法的軸承故障診斷研究[J];振動(dòng)與沖擊;2013年02期

7 羅方;;基于小波變換的信號(hào)去噪研究[J];科技風(fēng);2012年16期

8 林近山;;基于互補(bǔ)的總體經(jīng)驗(yàn)?zāi)Ｊ椒纸馑惴ǖ凝X輪箱故障診斷[J];機(jī)械傳動(dòng);2012年08期

9 王衍學(xué);何正嘉;訾艷陽(yáng);袁靜;;基于LMD的時(shí)頻分析方法及其機(jī)械故障診斷應(yīng)用研究[J];振動(dòng)與沖擊;2012年09期

10 楊宇;王歡歡;程軍圣;鄒憲軍;;基于LMD的包絡(luò)譜特征值在滾動(dòng)軸承故障診斷中的應(yīng)用[J];航空動(dòng)力學(xué)報(bào);2012年05期

相關(guān)博士學(xué)位論文 前1條

1 楊揚(yáng);參數(shù)化時(shí)頻分析理論、方法及其在工程信號(hào)分析中的應(yīng)用[D];上海交通大學(xué);2013年

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