本文選題：LMD分解　切入點(diǎn)：故障特征提取　出處：《燕山大學(xué)》2013年碩士論文

【摘要】：故障診斷是研究設(shè)備運(yùn)行狀態(tài)信息的變化，進(jìn)而識別其運(yùn)行狀態(tài)的科學(xué)，隨著現(xiàn)代化工業(yè)生產(chǎn)的不斷發(fā)展，機(jī)械設(shè)備故障診斷技術(shù)得到了廣泛的關(guān)注。在故障診斷中，，機(jī)械振動(dòng)信號的故障特征提取是其研究的瓶頸，信號的處理和分析是特征提取最常用的方法。在實(shí)際應(yīng)用中，機(jī)械振動(dòng)信號都是非平穩(wěn)的，對這類信號進(jìn)行準(zhǔn)確地分析和處理，是特征提取成功與否的關(guān)鍵所在。 近年來，信號處理的時(shí)頻分析方法在機(jī)械故障診斷領(lǐng)域得到了迅速的發(fā)展。傳統(tǒng)以Fourier變換為基礎(chǔ)的信號分析方法在非平穩(wěn)信號處理方面不能達(dá)到理想效果。本文重點(diǎn)研究基于局部均值分解(LMD)的時(shí)頻分析方法及其在旋轉(zhuǎn)機(jī)械故障特征提取方面的應(yīng)用。 首先，研究瞬時(shí)頻率、單分量和多分量信號、調(diào)頻調(diào)幅信號的基本概念；分析LMD的基本原理及算法，并與經(jīng)驗(yàn)?zāi)B(tài)分解(EMD)進(jìn)行對比；闡述瞬時(shí)頻率的求取方法：“直接法”、基于LMD的Hilbert變換法、能量算子解調(diào)法，分析各方法不足，在此基礎(chǔ)上提出三點(diǎn)對稱差分能量算子解調(diào)法，仿真驗(yàn)證該方法抑制LMD端點(diǎn)效應(yīng)的有效性。 其次，分析噪聲對LMD的影響，針對模態(tài)混疊，研究小波半軟閾值去噪和LMD相關(guān)度降噪技術(shù)，將兩者結(jié)合提出一種故障特征提取的方法，采用理論分析和機(jī)械故障信號相結(jié)合驗(yàn)證上述方法在改善時(shí)頻分析性能方面的有效性。 最后，研究時(shí)頻熵概念，提出局域時(shí)頻熵理論，將其與LMD結(jié)合提取旋轉(zhuǎn)機(jī)械故障特征，并進(jìn)行了仿真實(shí)驗(yàn)的驗(yàn)證。
[Abstract]:Fault diagnosis is a science to study the change of equipment operation state information and then identify its running state. With the development of modern industrial production, the technology of mechanical equipment fault diagnosis has been paid more and more attention.In fault diagnosis, fault feature extraction of mechanical vibration signal is the bottleneck of its research, and signal processing and analysis is the most commonly used method of feature extraction.In practical applications, mechanical vibration signals are non-stationary, and accurate analysis and processing of these signals is the key to the success of feature extraction.In recent years, time-frequency analysis of signal processing has been rapidly developed in the field of mechanical fault diagnosis.The traditional signal analysis method based on Fourier transform can not achieve ideal results in non-stationary signal processing.This paper focuses on the time-frequency analysis method based on local mean decomposition (LMD) and its application in fault feature extraction of rotating machinery.First of all, the basic concepts of instantaneous frequency, single component and multi-component signals, frequency modulation signal are studied, the basic principle and algorithm of LMD are analyzed, and compared with empirical mode decomposition (EMD), the method of obtaining instantaneous frequency is described as "direct method".Based on LMD's Hilbert transform method and energy operator demodulation method, the deficiency of each method is analyzed. Based on this, a three-point symmetric differential energy operator demodulation method is proposed. The simulation results show that the proposed method is effective in suppressing the LMD endpoint effect.Secondly, the effect of noise on LMD is analyzed. Aiming at modal aliasing, wavelet semi-soft threshold de-noising and LMD correlation de-noising are studied, and a fault feature extraction method is proposed.Theoretical analysis and mechanical fault signal are used to verify the effectiveness of the proposed method in improving the performance of time-frequency analysis.Finally, the concept of time-frequency entropy is studied, and the local time-frequency entropy theory is proposed, which is combined with LMD to extract the fault features of rotating machinery.
【學(xué)位授予單位】：燕山大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2013
【分類號】：TH165.3

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 楊宇;楊麗湘;程軍圣;;基于LMD和AR模型的轉(zhuǎn)子系統(tǒng)故障診斷方法[J];湖南大學(xué)學(xué)報(bào)(自然科學(xué)版);2010年09期

2 羅一新;機(jī)械故障診斷技術(shù)趨向分析[J];機(jī)床與液壓;2002年02期

3 劉衛(wèi)兵;李志農(nóng);蔣靜;易小兵;;局域均值分解方法在機(jī)械故障診斷中的應(yīng)用[J];機(jī)床與液壓;2011年01期

4 林京,屈梁生;信號時(shí)頻熵及其在齒輪裂紋識別中的應(yīng)用[J];機(jī)械傳動(dòng);1998年02期

5 徐春生;王太勇;;基于EMD和ANFIS的自適應(yīng)噪聲消除研究[J];機(jī)械強(qiáng)度;2009年02期

6 程軍圣;張亢;楊宇;;基于噪聲輔助分析的總體局部均值分解方法[J];機(jī)械工程學(xué)報(bào);2011年03期

7 明安波;褚福磊;張煒;;滾動(dòng)軸承復(fù)合故障特征分離的小波-頻譜自相關(guān)方法[J];機(jī)械工程學(xué)報(bào);2013年03期

8 胡勁松;楊世錫;任達(dá)千;;基于樣條的振動(dòng)信號局域均值分解方法[J];數(shù)據(jù)采集與處理;2009年01期

9 姜洪開,何正嘉,段晨東,陳雪峰;自適應(yīng)冗余第2代小波設(shè)計(jì)及齒輪箱故障特征提取[J];西安交通大學(xué)學(xué)報(bào);2005年07期

10 于德介,張嵬,程軍圣,楊宇;基于EMD的時(shí)頻熵在齒輪故障診斷中的應(yīng)用[J];振動(dòng)與沖擊;2005年05期

相關(guān)博士學(xué)位論文 前3條

1 鞠萍華;旋轉(zhuǎn)機(jī)械早期故障特征提取的時(shí)頻分析方法研究[D];重慶大學(xué);2010年

2 孟宗;軋機(jī)傳動(dòng)系統(tǒng)扭振測量模型及實(shí)驗(yàn)研究[D];燕山大學(xué);2008年

3 任達(dá)千;基于局域均值分解的旋轉(zhuǎn)機(jī)械故障特征提取方法及系統(tǒng)研究[D];浙江大學(xué);2008年

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