本文選題：提升機(jī)齒輪箱 + 通風(fēng)機(jī)��； 參考：《河南理工大學(xué)》2012年碩士論文

【摘要】：煤礦大型固定機(jī)械，如提升機(jī)和主通風(fēng)機(jī)是煤礦安全生產(chǎn)的至關(guān)重要設(shè)備，其運(yùn)行狀態(tài)的好壞直接影響到煤礦生產(chǎn)的經(jīng)濟(jì)效益。此類(lèi)設(shè)備不僅維修工期長(zhǎng)、費(fèi)用高、更為嚴(yán)重的是易發(fā)生突發(fā)事故甚至惡性事故造成企業(yè)巨大的經(jīng)濟(jì)損失及不良的社會(huì)影響。因此，設(shè)法盡早從這些可能引發(fā)災(zāi)難性事故的故障進(jìn)行有效檢測(cè)對(duì)提高煤礦機(jī)械的可靠性和安全性具有重要意義，而其早期檢測(cè)的關(guān)鍵技術(shù)之一就是故障信號(hào)的特征提取。 礦井提升機(jī)和主通風(fēng)機(jī)結(jié)構(gòu)復(fù)雜、工作環(huán)境較差，外界干擾強(qiáng)，使得表征故障的特征信號(hào)比較復(fù)雜，因沖擊和調(diào)制所引發(fā)的動(dòng)態(tài)信號(hào)往往具有非平穩(wěn)性，為此發(fā)展能有效處理非平穩(wěn)特征信號(hào)的方法并將其應(yīng)用于礦井提升機(jī)和主通風(fēng)機(jī)的故障特征提取具有重要的實(shí)際使用價(jià)值。 本文主要以提升機(jī)齒輪箱和通風(fēng)機(jī)傳動(dòng)系統(tǒng)為研究對(duì)象，利用諧波小波分析方法，，對(duì)能突出表征設(shè)備故障的特征提取方法進(jìn)行了深入研究。主要內(nèi)容包括： （1）對(duì)傅里葉變換、小波分析和諧波小波分析理論進(jìn)行了介紹，并比較了這些方法在煤礦機(jī)械故障特征提取中的優(yōu)勢(shì)和局限。 （2）對(duì)提升機(jī)齒輪箱和通風(fēng)機(jī)傳動(dòng)系統(tǒng)常見(jiàn)的振動(dòng)和故障機(jī)理進(jìn)行了研究。 （3）介紹了諧波小波理論，利用諧波小波變換的優(yōu)點(diǎn)，將其用于對(duì)礦井主通風(fēng)機(jī)和提升機(jī)減速箱的故障診斷，成功從其振動(dòng)信號(hào)中提取出了故障特征信號(hào)，為煤礦大型固定機(jī)械的精密故障診斷提供了可靠依據(jù)。
[Abstract]:Large fixed machinery in coal mine, such as hoist and main ventilator, is the most important equipment for coal mine safety production, and its running condition directly affects the economic benefit of coal mine production. This kind of equipment not only has a long maintenance period and high cost, but also is prone to sudden accidents or even malignant accidents, resulting in huge economic losses and adverse social impact of enterprises. Therefore, it is of great significance to improve the reliability and safety of coal mine machinery by trying to detect the faults that may lead to catastrophic accidents as soon as possible, and one of the key techniques of early detection is the feature extraction of fault signals. The structure of mine hoist and main ventilator is complex, the working environment is poor, and the external disturbance is strong, which makes the characteristic signal which characterizes the fault more complicated, and the dynamic signal caused by shock and modulation is often non-stationary. Therefore, it is of great practical value to develop a method to deal with the non-stationary characteristic signal and apply it to the fault feature extraction of mine hoist and main ventilator. In this paper, the hoist gearbox and fan transmission system are taken as the research object, and the feature extraction method which can highlight the fault of the equipment is studied deeply by using the harmonic wavelet analysis method. The main elements include: This paper introduces the theory of Fourier transform, wavelet analysis and harmonic wavelet analysis, and compares the advantages and limitations of these methods in fault feature extraction of coal mine machinery. The vibration and fault mechanism of hoist gearbox and fan drive system are studied. This paper introduces the theory of harmonic wavelet and applies it to fault diagnosis of mine main ventilator and hoist reducer by using the advantage of harmonic wavelet transform. The fault characteristic signal is successfully extracted from its vibration signal. It provides reliable basis for precise fault diagnosis of large fixed machinery in coal mine.
【學(xué)位授予單位】：河南理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類(lèi)號(hào)】：TH165.3;TD407

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本文編號(hào)：1977125