發(fā)布時(shí)間：2018-03-24 01:34

  本文選題：循環(huán)平穩(wěn)　切入點(diǎn)：循環(huán)雙譜　出處：《昆明理工大學(xué)》2017年碩士論文

【摘要】：特征提取在機(jī)械設(shè)備的狀態(tài)監(jiān)測和故障診斷中是一個(gè)非常重要的信號(hào)處理問題。自上世紀(jì)80年代以來,為了滿足對(duì)機(jī)械故障進(jìn)行精確診斷的需要,對(duì)非線性、非高斯、非平穩(wěn)的信號(hào)處理技術(shù)在機(jī)械故障診斷領(lǐng)域受到越來越多的關(guān)注。由于高階統(tǒng)計(jì)量有抑制高斯噪聲的性質(zhì),本文采用理論和Matlab仿真實(shí)驗(yàn)相結(jié)合的手段,研究了基于循環(huán)雙譜的機(jī)械振動(dòng)仿真信號(hào)的分析。首先,本文擬采用高階統(tǒng)計(jì)分析方法分析平穩(wěn)隨機(jī)信號(hào),高階統(tǒng)計(jì)量可以抑制高斯噪聲,提取非平穩(wěn)、非線性、非高斯故障信號(hào)的特征信息,對(duì)故障的診斷具有重要的意義。但隨著階數(shù)越來越高,計(jì)算量就越來越大,而三階統(tǒng)計(jì)量既能有效地抑制高斯噪聲,又能提取非線性信號(hào)特征信息,計(jì)算量是相對(duì)最小的,因此本文著重分析了雙譜估計(jì)。其次,研究循環(huán)平穩(wěn)信號(hào)的定義和分析方法。(1)分析了一階和二階循環(huán)統(tǒng)計(jì)量的基本理論,利用循環(huán)自相關(guān)函數(shù)的切片譜能在循環(huán)頻率域分離載波頻率信息和調(diào)制頻率信息的優(yōu)點(diǎn),可以更容易地在循環(huán)頻率低頻率段提取出故障的調(diào)制頻率信息;使用周期頻率和頻率特性之間的相關(guān)特點(diǎn),用切片圖可以提煉出有用的信息,然后分析頻率信息的特點(diǎn)。(2)機(jī)械振動(dòng)信號(hào)可以產(chǎn)生幅度調(diào)制信號(hào)、頻率調(diào)制信號(hào)、多載波頻率調(diào)制信號(hào)、多調(diào)制源調(diào)幅信號(hào)和多載波調(diào)幅信號(hào)等,為了能更加準(zhǔn)確的了解機(jī)械設(shè)備的故障特征情況,研究這些復(fù)雜的調(diào)制信號(hào)解調(diào)方法,發(fā)現(xiàn)循環(huán)自相關(guān)解調(diào)分析可以有效地分離出調(diào)制信號(hào)、載波信息等,具有較強(qiáng)的噪聲抑制特性。最后,本文對(duì)機(jī)械振動(dòng)的仿真信號(hào)進(jìn)行了仿真實(shí)驗(yàn)研究,因循環(huán)雙譜涉及四維分析計(jì)算量較大,而自相關(guān)切片譜不能有效抑制高斯噪聲,結(jié)合兩者的特點(diǎn),本文將循環(huán)自相關(guān)函數(shù)切片的方法引入到循環(huán)雙譜分析,提出了采用循環(huán)雙譜載波頻率切片法提取故障特征,可以直觀的表達(dá)譜分析的結(jié)果,有效的提取出故障特征頻率信息。同時(shí),本文還將故障信號(hào)先小波去噪再進(jìn)行雙譜分析,實(shí)驗(yàn)仿真結(jié)果表明,小波去噪后的循環(huán)雙譜載波頻率切片譜圖中的干擾明顯減少,特征頻率信息更突出的表現(xiàn)了出來。
[Abstract]:Feature extraction in condition monitoring and fault diagnosis of mechanical equipment is a very important signal processing problems. Since the last century since 80s, in order to meet the needs for accurate diagnosis of mechanical faults of nonlinear, non Gauss, non-stationary signal processing technology has attracted more and more attention in the field of mechanical fault diagnosis due to the high. Order statistics properties suppress Gauss noise, this paper uses the theory and method of combining Matlab simulation experiment, studied the simulation analysis of mechanical vibration signal based on the cyclic bispectrum. Firstly, this paper uses high order statistics method analysis of stationary random signal, high-order statistics can suppress Gauss noise, nonlinear, non-stationary extraction. Non Gauss characteristic information of fault signals, has important significance for fault diagnosis. But with the order of computation is more and more high, more and more large, and the three order statistics Quantity can effectively suppress the Gauss noise, and can extract the nonlinear feature information of signal, the amount of calculation is relatively minimal, so this paper focuses on the analysis of the bispectrum estimation. Secondly, the research of cyclostationary signal definition and analysis. (1) the analysis of the basic theory of one order and two order cyclic statistics, the advantages of recycling the autocorrelation function of the slice spectrum can separate carrier frequency modulation information and frequency information in cyclic frequency domain, can be more easily extracted rate modulation frequency information of fault in cycle frequency; related characteristics of cycle frequency and frequency characteristics of the section map can extract useful information, and then analyzes the characteristics the frequency of information. (2) the mechanical vibration signal can generate the amplitude modulation signal, frequency modulation signal, multi carrier frequency modulation signal, multi modulation amplitude modulation signal and multi carrier modulation signals, in order to A more accurate understanding of the fault features of mechanical equipment status and Research on these complex modulation signal demodulation method, cyclic autocorrelation demodulation analysis can effectively separate the modulated signal carrier information, has strong noise suppression characteristics. Finally, the simulation of the mechanical vibration signal was studied by simulation, cyclic bispectrum the analysis involves a large amount of calculation and correlation dimension, slice spectrum can not effectively suppress Gauss noise, combined with the characteristics of this method, the cyclic autocorrelation function is introduced to slice cyclic bispectrum analysis, the carrier frequency of cyclic bispectrum slice method to extract fault features, the results of the analysis can be intuitive expression, extract the characteristic frequency of fault information effectively. At the same time, this paper will first fault signal wavelet denoising and bispectrum analysis, simulation results show that after wavelet denoising The interference in the cyclic bispectral frequency slice spectrum is obviously reduced, and the characteristic frequency information is more prominent.

【學(xué)位授予單位】：昆明理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TH17

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本文編號(hào)：1656143