基于谐波小波分析的矿山机械故障诊断研究

发布时间：2018-06-04 11:03

本文选题：提升机齿轮箱 + 通风机　；参考：《河南理工大学》2012年硕士论文

【摘要】：煤矿大型固定机械，如提升机和主通风机是煤矿安全生产的至关重要设备，其运行状态的好坏直接影响到煤矿生产的经济效益。此类设备不仅维修工期长、费用高、更为严重的是易发生突发事故甚至恶性事故造成企业巨大的经济损失及不良的社会影响。因此，设法尽早从这些可能引发灾难性事故的故障进行有效检测对提高煤矿机械的可靠性和安全性具有重要意义，而其早期检测的关键技术之一就是故障信号的特征提取。矿井提升机和主通风机结构复杂、工作环境较差，外界干扰强，使得表征故障的特征信号比较复杂，因冲击和调制所引发的动态信号往往具有非平稳性，为此发展能有效处理非平稳特征信号的方法并将其应用于矿井提升机和主通风机的故障特征提取具有重要的实际使用价值。本文主要以提升机齿轮箱和通风机传动系统为研究对象，利用谐波小波分析方法，，对能突出表征设备故障的特征提取方法进行了深入研究。主要内容包括：（1）对傅里叶变换、小波分析和谐波小波分析理论进行了介绍，并比较了这些方法在煤矿机械故障特征提取中的优势和局限。（2）对提升机齿轮箱和通风机传动系统常见的振动和故障机理进行了研究。（3）介绍了谐波小波理论，利用谐波小波变换的优点，将其用于对矿井主通风机和提升机减速箱的故障诊断，成功从其振动信号中提取出了故障特征信号，为煤矿大型固定机械的精密故障诊断提供了可靠依据。
[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.
【学位授予单位】：河南理工大学
【学位级别】：硕士
【学位授予年份】：2012
【分类号】：TH165.3;TD407

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