【作者】 王春林；

【导师】 梅卫江；

【作者基本信息】 石河子大学 ， 机械制造及其自动化， 2010， 硕士

【摘要】 齿轮箱作为机械设备中一种必不可少的连接和传递动力的通用零部件,在现代工业发展中具有广泛的应用。同时齿轮又是最容易损坏的机械零件之一。齿轮系统工作过程中发生的齿轮断齿、点蚀、滚动轴承的疲劳剥落、轴弯曲等,都会产生周期性的脉冲冲击力,产生振动信号的调制现象,在频谱上表现为在啮合频率或固有频率两侧出现间隔均匀的调制边频带。从信号中提取调制信息,分析其强度和频次就可以判断零件损伤的程度和部位。本文针对齿轮箱齿轮振动信号的振动机理和振动特点,以齿轮箱中齿轮正常、齿轮磨损和齿轮断齿的故障信号为研究对象,利用DASP及MATLAB开展基于EMD的故障特征信息提取技术的研究；本文主要进行了以下几个方面的工作：1)阐述了齿轮箱故障诊断技术的发展状况,齿轮故障振动的机理和振动特点。2)详细分析和讨论了齿轮箱齿轮故障诊断的常用方法。并充分讨论和分析了齿轮的各种振动信号分析方法,包括时域分析、频域分析和EMD分解方法以及各种时、频域特征提取的方法。3)基于DASP系统搭建了齿轮箱齿轮振动信号数据采集及分析系统实验平台,采集齿轮三种状态下的振动信号,并通过时、频分析提取了齿轮故障状态的时域和频域的特征信息。4)研究了经验模态分解的基本理论及在齿轮箱故障振动信号中的实际应用,对采集到齿轮三种状态下的振动信号成功地通过EMD提取了故障信号的特征信息,为识别故障类型提供了有效的分析手段。5)利用主成分与聚类相结合的分析方法对齿轮三种状态下采集到的振动信号的数据的时域和频域内的特征参数进行故障模式分类识别,再与基于EMD提取的齿轮故障特征进行对比,从而也是对基于EMD的齿轮故障特征信息提取与诊断分析的结果进行了逆向的验证。6)通过多元统计分析,对基于EMD的AR模型中提取齿轮三种状态下的振动信号的特征向量模板,进行聚类分析得到齿轮不同状态的模式分类,研究比较证明基于EMD的AR模型提取的故障特征信息能够反映齿轮不同的运行状态。并将所提取到的特征向量模板建立了一个基准模型,且用待检状态对模型进行了验证。证明基于EMD的AR模型在齿轮箱齿轮故障诊断中的可行性与准确性。通过研究,证明了利用虚拟仪器系统对齿轮箱快速诊断的可能性。同时也为农业生产实现田间现场简易诊断奠定一定的理论基础。更多还原

【Abstract】 As a necessary part of connecting and force transmission in mechanical equipment, gearbox system is played a comprehensive role, with the development of modern industry. And gearbox is also the mechanical part which is damaged easiest. When the gear system works, periodic impulse impact force will produce owing to the faults of gear abruption or corrosive pitting, etc. So phenomena of modulation produced at this moment and spectrum of the vibration signal has symmetrical modulation side-bands besides the meshing frequency or natural frequency. Extracted modulation information and its strength and modulation frequency order can be analyzed which can be used to judge the fault parts. It is a compressively used in mechanical fault diagnosis.In order to effectively extract the feature vector of fault in the vibration signals of gear and achieve the purposes of identifying and eliminating the faults, according to the mechanism and characteristics of gearbox vibration, the following researches is based on EMD by using DASP and MATLAB. In this paper, the fault gears vibration signal of normal working state, abrasion working state and rupture working state are the studied objects.1) The development of technology of fault diagnosis for gearbox, the mechanism and characteristics of gear and bear vibration are discussed.2) Some existing gear fault diagnosis methods, which are mainly based on the basic principles and methods of vibration analysis, are analyzed and discussed in detail. The analysis methods of vibration signals are discussed thoroughly, including time domain analysis, frequency domain analysis and EMD. The way of extracting the feature vector of fault in the time domain and frequency domain is also discussed.3) The gearbox vibration experiment terrace is designed. The vibration signals of gear with different state are extracted. The feature vector of fault in the time domain and frequency domain is also extracted.4) The fundamental theory of EMD analysis and its application are introduced on the application of fault diagnosis for gearbox. Then by means of the EMD, the fault feature vectors of fault are successfully extracted and this effective method is employed to identify the fault pattern.5) Through PCA and cluster analysis, independent principal components are then extracted from the abnormal data.6) Through multivariate statistics analysis, the analysis method of AR model based on EMD is proposed and applied to gear fault diagnosis. The research results show that the work state and fault patterns of gear can be identified by the proposed method efficiently.Experimental results show these proposed methods, which can protrude the fault character of the gearbox, are practical in gearbox local fault diagnosis and of important meaning.更多还原