【摘要】：在當(dāng)代中國各類經(jīng)濟(jì)行業(yè)中,尤其是在制造、運輸、能源、冶煉、石油、國防科技等行業(yè)中的核心零部件,其工作環(huán)境大多具有腐蝕、高溫、高壓等復(fù)雜、惡劣的環(huán)境特點,設(shè)備中的核心零部件和重要機(jī)械結(jié)構(gòu)不可避免的會發(fā)生不同程度的故障。軸承由于其零件所在位置的特殊性,往往容易發(fā)生疲勞剝落和點蝕等故障,一旦發(fā)生故障,則會導(dǎo)致嚴(yán)重的經(jīng)濟(jì)財產(chǎn)損失,減少設(shè)備的運行壽命。因此本文主要針對滾動軸承故障信號作為研究對象,對故障信號的降噪與解調(diào)方法進(jìn)行研究和討論,論文的主要工作內(nèi)容包括:(1)分析了滾動軸承故障信號處理中常用到的幾種降噪方法,對比了小波與小波包相關(guān)的降噪理論,針對故障信號中往往含有大量背景噪聲難以去除的情況,提出了基于小波包熵值與EMD分解相結(jié)合的降噪方法,該方法在經(jīng)過小波包熵值的有效降噪后再進(jìn)行EMD分解,能夠自適應(yīng)的從故障信號中提取出微弱的故障成分。(2)提出了基于小波包熵值與自相關(guān)分析相結(jié)合的降噪方法,利用自相關(guān)分析能夠突出故障信號周期性的特性,將小波包熵值降噪法與之相結(jié)合,在小波包熵值降噪去除大量噪聲的同時利用自相關(guān)分析進(jìn)一步抑制噪聲,在保留原有故障調(diào)制信息的基礎(chǔ)上突出信號的周期性。(3)對比了各類信號解調(diào)方法的優(yōu)缺點,分析了在相同降噪方法下,能量算子解調(diào)法與Hilbert解調(diào)法的解調(diào)效果,結(jié)合以上兩種信號降噪分析方法,提出了基于小波包熵值與EMD的能量算子解調(diào)法;基于小波包熵值與自相關(guān)分析的能量算子解調(diào)法,從而準(zhǔn)確的判斷故障位置。(4)引入EMD與EEMD多分量分析,將小波包熵值降噪,自相關(guān)分析,能量算子解調(diào)法與之結(jié)合起來,提出了一種基于多分量分析與自相關(guān)分析相結(jié)合的故障信號能量算子解調(diào)法,該方法在小波包熵值有效降噪基礎(chǔ)上,能夠?qū)收闲盘栠M(jìn)行有效的解調(diào),實現(xiàn)對故障位置的判別。本文的研究工作為旋轉(zhuǎn)機(jī)械故障信號的解調(diào)分析和診斷提供了一條新的方法思路,對于滾動軸承故障信號的解調(diào)處理上具有一定的參考。
[Abstract]:In contemporary China's various economic industries, especially in manufacturing, transportation, energy, smelting, petroleum, national defense science and technology and other industries, the core components, its working environment is mostly corrosion, high temperature, high pressure and other complex, harsh environmental characteristics. The core parts and important mechanical structure of the equipment will inevitably break down in varying degrees. Because of the particularity of the location of the bearing parts, it is easy to occur fatigue spalling and pitting. Once the failure occurs, it will lead to serious economic property losses and reduce the service life of the equipment. Therefore, in this paper, the noise reduction and demodulation methods of rolling bearing fault signals are studied and discussed. The main contents of this paper are as follows: (1) several noise reduction methods used in rolling bearing fault signal processing are analyzed, and the noise reduction theory related to wavelet and wavelet packet is compared. In view of the fact that it is difficult to remove a large amount of background noise in the fault signal, a method based on the combination of wavelet packet entropy and EMD decomposition is proposed, which decomposes the EMD after the effective denoising of the wavelet packet entropy. It can self-adaptively extract weak fault components from fault signals. (2) A noise reduction method based on wavelet packet entropy and autocorrelation analysis is proposed, and the periodicity of fault signals can be highlighted by using autocorrelation analysis. The wavelet packet entropy denoising method is combined with the wavelet packet entropy value to remove a large amount of noise, and the autocorrelation analysis is used to further suppress the noise. On the basis of preserving the original fault modulation information, the periodicity of the signal is highlighted. (3) the advantages and disadvantages of various signal demodulation methods are compared, and the demodulation effects of the energy operator demodulation method and the Hilbert demodulation method under the same noise reduction method are analyzed. The energy operator demodulation method based on wavelet packet entropy value and EMD, the energy operator demodulation method based on wavelet packet entropy value and autocorrelation analysis, and the energy operator demodulation method based on wavelet packet entropy and autocorrelation analysis are proposed. In order to accurately judge the fault location. (4) EMD and EEMD multicomponent analysis are introduced to reduce the noise of wavelet packet entropy, autocorrelation analysis, energy operator demodulation method and the combination of them. A fault signal energy operator demodulation method based on multi-component analysis and autocorrelation analysis is proposed. Based on the effective denoising of wavelet packet entropy, the fault signal can be demodulated effectively and the fault location can be distinguished. The research work in this paper provides a new method for the demodulation analysis and diagnosis of the fault signals of rotating machinery, and has a certain reference for the demodulation and processing of the fault signals of rolling bearings.
【學(xué)位授予單位】：內(nèi)蒙古科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：TH133.3

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