本文選題：經(jīng)驗(yàn)?zāi)B(tài)分解 + 單調(diào)性一致��； 參考：《湖南科技大學(xué)》2016年碩士論文

【摘要】：經(jīng)驗(yàn)?zāi)B(tài)分解(Empirical Mode Decomposition,EMD)是處理非線性和非平穩(wěn)信號(hào)的有效方法。該方法根據(jù)自身信號(hào)的特點(diǎn),將信號(hào)分解成若干個(gè)本征模態(tài)函數(shù)IMF之和,彌補(bǔ)了短時(shí)傅里葉、小波分解和Wigner-Ville分布的不足。目前,EMD廣泛應(yīng)用于機(jī)械故障診斷、生物醫(yī)學(xué)信號(hào)分析和通訊信號(hào)分析等領(lǐng)域。本文對(duì)EMD的理論進(jìn)行了分析,以算法本身固有的缺點(diǎn)為突破口,對(duì)EMD中的端點(diǎn)效應(yīng)問題和模態(tài)混疊問題進(jìn)行了研究,并給出了相應(yīng)的解決方案。研究的主要內(nèi)容如下:(1)針對(duì)用的機(jī)械故障信號(hào)時(shí)頻分析方法,如短時(shí)Fourier變換、Wigner-Ville分布、小波變換等,總結(jié)這些方法的特點(diǎn)及不足,在此基礎(chǔ)上引出Hilbert-Huang變換(HHT),指出EMD分解算法中有幾個(gè)問題需要解決,如端點(diǎn)效應(yīng)、模態(tài)混疊。(2)利用信號(hào)波形單調(diào)性一致來處理信號(hào)端點(diǎn)效應(yīng)問題。經(jīng)驗(yàn)?zāi)B(tài)分解需通過極值點(diǎn)描述信號(hào)上下包絡(luò)線,但是信號(hào)兩端邊界的極大值和極小值不好估計(jì),包絡(luò)線就存在著變數(shù),這樣經(jīng)驗(yàn)?zāi)B(tài)分解過程就會(huì)產(chǎn)生邊界誤差,隨著分解進(jìn)行邊界誤差會(huì)向內(nèi)傳播,從而污染內(nèi)部數(shù)據(jù),導(dǎo)致分解結(jié)果不合理。通過分析幾種典型的抑制端點(diǎn)效應(yīng)的方法,把單調(diào)性一致法引入EMD,以獲得信號(hào)端點(diǎn)極值點(diǎn),這種方法簡(jiǎn)單而且可以有效地抑制端點(diǎn)效應(yīng)。(3)提出奇異值分解去噪法來抑制EMD過程中的事件性模態(tài)混疊問題和提出了基于能量分離的方法(SEMD)避免了非事件性模態(tài)混疊現(xiàn)象。事件性模態(tài)混疊首先通過聯(lián)合平穩(wěn)度的自適應(yīng)模態(tài)解混疊方法篩選出異常信號(hào)區(qū)間,再利用奇異值分解去噪消除異常事件,使異常事件不再那么明顯,從而使信號(hào)包絡(luò)更自然,可以有效抑制模態(tài)混疊現(xiàn)象,提高EMD的整體分解效果,并與傳統(tǒng)的EMD方法對(duì)比,改進(jìn)的方法能有效抑制模態(tài)混疊問題。非事件性模態(tài)混疊采用奇異值分解將能量高的信號(hào)重新聚合,能量低的重新聚合,再進(jìn)行EMD分解,并進(jìn)行了模擬驗(yàn)證,結(jié)果表明SEMD方法能有效的分離出信號(hào)成分,與直接進(jìn)行EMD分解相比較,該方法具有明顯優(yōu)越性。(4)基于以上的研究,提出了基于單調(diào)性一致和奇異值分解的EMD方法(MSEMD),并選用美國(guó)凱斯西儲(chǔ)大學(xué)軸承數(shù)據(jù)中心的數(shù)據(jù)進(jìn)行分析,對(duì)MSEMD和經(jīng)驗(yàn)?zāi)B(tài)分解進(jìn)行故障頻率識(shí)別對(duì)比分析,得出了MSEMD分解效果優(yōu)于經(jīng)驗(yàn)?zāi)B(tài)分解,可以有效的識(shí)別軸承故障。
[Abstract]:Empirical Mode decomposition (EMD) is an effective method for dealing with nonlinear and non-stationary signals.According to the characteristics of the signal, the method decomposes the signal into the sum of several intrinsic mode functions (IMF), which makes up for the shortage of short-time Fourier transform, wavelet decomposition and Wigner-Ville distribution.At present, EMD is widely used in mechanical fault diagnosis, biomedical signal analysis and communication signal analysis.In this paper, the theory of EMD is analyzed, and the endpoints effect and modal aliasing in EMD are studied with the inherent shortcomings of the algorithm as the breakthrough point, and the corresponding solutions are given.The main contents of this paper are as follows: (1) aiming at the time-frequency analysis methods of mechanical fault signals, such as short time Fourier transform Wigner-Ville distribution, wavelet transform and so on, the characteristics and shortcomings of these methods are summarized.On this basis, the Hilbert-Huang transform is introduced, and several problems need to be solved in the EMD decomposition algorithm, such as endpoint effect, mode aliasing.Empirical mode decomposition (EMD) is required to describe the upper and lower envelope of the signal through extreme points. However, the maximum and minimum of the two ends of the signal are difficult to estimate, and the envelope exists variables, so the empirical mode decomposition process will produce boundary errors.The boundary error will propagate inward with the decomposition, which pollutes the internal data and leads to unreasonable decomposition results.By analyzing several typical methods to suppress the endpoint effect, the monotonic consistency method is introduced into the EMD to obtain the extreme point of the signal endpoint.This method is simple and effective to suppress the endpoint effect. (3) the singular value decomposition (SVD) denoising method is proposed to suppress the event-mode aliasing in the EMD process and the energy seperation-based method (SEMD-based) is proposed to avoid the non-event-mode aliasing.Event mode aliasing first selects the interval of abnormal signals by combining the adaptive mode de-aliasing method of stationary degree, and then uses singular value decomposition to remove the abnormal events, which makes the abnormal events less obvious, thus making the envelope of the signals more natural.It can effectively suppress the phenomenon of mode aliasing and improve the overall decomposition effect of EMD. Compared with the traditional EMD method, the improved method can effectively suppress the modal aliasing problem.Non-event mode aliasing uses singular value decomposition (SVD) to reaggregate high-energy signals, low-energy reaggregates, and then EMD decomposition. The simulation results show that the SEMD method can effectively separate the signal components.Compared with direct EMD decomposition, this method has obvious advantages. Based on the above research, a EMD method based on monotonicity uniformity and singular value decomposition is proposed, and the data from the Cass Western Reserve University bearing data Center are selected for analysis.Comparing the fault frequency identification between MSEMD and empirical mode decomposition, it is concluded that the effect of MSEMD decomposition is better than that of empirical mode decomposition, and it can effectively identify bearing faults.
【學(xué)位授予單位】：湖南科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：TH17

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 王洪明;郝旺身;韓捷;董辛e，

本文編號(hào)：1760831