本文選題：主通風(fēng)機 + 振動��； 參考：《中國礦業(yè)大學(xué)》2015年碩士論文

【摘要】：煤礦主通風(fēng)機是保證煤礦安全生產(chǎn)的關(guān)鍵設(shè)備,是煤礦井下通風(fēng)的主要動力來源,通風(fēng)機的正常工作可以向礦井工作面中輸送新鮮的空氣,保證井下工作環(huán)境良好,所以煤礦主通風(fēng)機的無故障的連續(xù)工作需要得到保證,這對整個煤礦的生產(chǎn)工作都是一種保證。但是煤礦主通風(fēng)機需要長時間帶負(fù)荷運轉(zhuǎn),所以在此長期的運行過程中,由于自身器件和外界因素的影響容易發(fā)生一些故障,發(fā)生故障的直接表現(xiàn)就是主風(fēng)機的振動異常,主通風(fēng)機的振動信號往往包含大量的故障信息,所以隨著技術(shù)進步和信號處理相關(guān)理論的發(fā)展,振動信號的特征提取也會有更廣闊的發(fā)展前景。主通風(fēng)機的振動信號通常為多分量的非平穩(wěn)、非線性信號,目前的常見時頻分析方法有短時傅里葉變換、Winger-Ville分布、小波變換、EMD等都具有一定的局限性,本文主要采用了局域均值分解(LMD)和小波閾值去噪相結(jié)合的方法對煤礦主通風(fēng)機的振動信號進行特征提取和分析,主要研究內(nèi)容如下:研究了常用的時頻分析方法(短時傅里葉變換、Winger-Ville分布、小波變換)在處理非平穩(wěn)信號上的表現(xiàn),分析出各自在非平穩(wěn)信號分析上的特點和不足。研究了經(jīng)驗?zāi)B(tài)分解(EMD)和局域均值分解(LMD)的基本原理和基本算法,并在MATLAB平臺上進行了仿真分析,并對兩種方法在端點處理上進行了比較分析,說明LMD算法更加適應(yīng)于非平穩(wěn)信號的分析。研究了小波閾值去噪方法在MATLAB平臺上的實現(xiàn),并與傅里葉去噪進行了對比分析實現(xiàn),得出小波閾值去噪的優(yōu)越性,并提出了基于局域均值分解(LMD)與小波閾值去噪相結(jié)合的方法對煤礦主通風(fēng)機的振動信號進行提取分析,并收到很好的效果。
[Abstract]:The main ventilator of coal mine is the key equipment to ensure the safe production of coal mine and the main power source of underground ventilation. The normal operation of the ventilator can transport fresh air to the mine face and ensure a good working environment.Therefore, the failure-free continuous operation of main ventilator in coal mine needs to be guaranteed, which is a guarantee for the whole production of coal mine.But the main ventilator in coal mine needs to run with load for a long time, so in the long running process, some faults are easy to occur due to the influence of its own devices and external factors, and the direct performance of the failure is the abnormal vibration of the main fan.The vibration signal of main ventilator often contains a lot of fault information, so with the development of technology and signal processing theory, the feature extraction of vibration signal will have a broader development prospect.The vibration signals of main ventilator are usually multicomponent non-stationary and nonlinear signals. The current time-frequency analysis methods include short time Fourier transform (STFT) Winger-Ville distribution, wavelet transform (EMD) and so on.In this paper, the local mean decomposition (LMD) method and wavelet threshold denoising method are used to extract and analyze the vibration signals of coal mine main ventilator.The main research contents are as follows: the performance of time-frequency analysis methods (short time Fourier transform Winger-Ville distribution, wavelet transform) in dealing with non-stationary signals is studied, and their characteristics and shortcomings in non-stationary signal analysis are analyzed.The basic principle and algorithm of empirical mode decomposition (EMD) and local mean decomposition (LMD) are studied. The simulation analysis is carried out on MATLAB platform, and the two methods are compared in endpoint processing.It shows that LMD algorithm is more suitable for non stationary signal analysis.The realization of wavelet threshold de-noising method on MATLAB platform is studied, and compared with Fourier de-noising method, the superiority of wavelet threshold de-noising method is obtained.A method based on local mean decomposition (LMD) and wavelet threshold de-noising is proposed to extract and analyze the vibration signal of coal mine main ventilator, and good results are obtained.
【學(xué)位授予單位】：中國礦業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：TD441

【參考文獻】

相關(guān)期刊論文 前4條

1 侯蒙蒙;許同樂;高朋飛;馮曉青;;基于LMD分解的風(fēng)機軸承故障信號提取研究[J];中國農(nóng)機化學(xué)報;2015年02期

2 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(自然科學(xué)版);2010年09期

3 胡愛軍;馬萬里;唐貴基;;基于集成經(jīng)驗?zāi)B(tài)分解和峭度準(zhǔn)則的滾動軸承故障特征提取方法[J];中國電機工程學(xué)報;2012年11期

4 張亢;程軍圣;楊宇;;基于自適應(yīng)波形匹配延拓的局部均值分解端點效應(yīng)處理方法[J];中國機械工程;2010年04期

相關(guān)博士學(xué)位論文 前2條

1 劉興杰;風(fēng)電輸出功率預(yù)測方法與系統(tǒng)[D];華北電力大學(xué);2011年

2 王衍學(xué);機械故障監(jiān)測診斷的若干新方法及其應(yīng)用研究[D];西安交通大學(xué);2009年

相關(guān)碩士學(xué)位論文 前1條

1 竇聯(lián)樂;煤礦主扇通風(fēng)系統(tǒng)的構(gòu)成及其控制方法的研究[D];長安大學(xué);2012年

，

本文編號：1743773