【作者】 冷军发；

【导师】 荆双喜；

【作者基本信息】 河南理工大学 ， 机械制造及其自动化， 2016， 博士

【摘要】 齿轮箱作为机械设备中必不可少的传递运动与动力的关键部件,广泛应用于现代工业各种大型、重型机械设备中,其工作和运行环境一般比较差。一旦出现故障可能导致整个机器不能正常工作,不仅增加了设备维护费用,还可能造成巨大的经济损失,甚至是人员伤亡。因此,研究齿轮箱故障诊断技术和方法具有重要的学术意义和应用价值。故障特征提取是齿轮箱状态监测与故障诊断的关键问题,特别在低速区时,因高频啮合振动、传递通道及强噪声等多方面因素影响,造成有效故障特征信息非常微弱,给齿轮箱的低频故障特征提取带来诸多难题。因此,有必要寻找合适的振动信号处理技术与方法,有效地从复杂振动信号中分离故障信号及提取微弱故障特征。基于盲源分离技术对齿轮箱振动信号的普适性,本文就齿轮箱恒转速工况条件下,深入地研究和丰富了基于盲源分离的低频故障特征提取新方法,并将其成功应用于试验齿轮箱和矿用齿轮箱的故障特征提取与故障诊断。主要研究内容有:(1)阐述了论文的选题背景和研究意义,分析了齿轮箱的时频分析方法、盲源分离等方面的国内外研究现状,针对盲源分离技术在齿轮箱低频故障特征提取存在的问题,确立了本文的研究路线和主要研究内容。(2)考虑到振动源信号可能直接混有源噪声,信号之间的统计独立性与非高斯性相对更弱,增加了齿轮箱微弱故障信号盲提取的难度。将源噪声引入盲源分离的线性瞬时混合和卷积混合模型中,使其更符合齿轮箱振动系统的实际情况,为含源噪声条件下的齿轮箱低频故障特征盲提取方法的提出奠定了基础。(3)约束独立分量分析(Constrained independent component analysis,cICA)算法对于多通道传感器测量噪声具有很强的免疫能力,但对源信号含源噪声的分析效果却很差。针对这个问题,提出了小波变换(Wavelet transform,WT)特征增强的cICA的齿轮箱故障特征提取方法,该方法可以减少其他振源及强噪声的干扰,提高信噪比,增强cICA的故障特征提取效果。将其应用于试验齿轮箱和矿用皮带输送机齿轮箱的故障诊断,分别提取出了表征各自故障的低频振动特征。(4)cICA算法要求观测信号数目不少于源信号数目,不能直接提取单通道测量信号的故障信息。集成经验模态分解(Ensemble empirical mode decomposition,EEMD)能够有效减少模态混叠和除噪,然而该算法会产生虚假分量。通过计算互相关系数与峭度来选择合适的本征模态函数(Intrinsic mode function,IMF)分量,并与原测量信号组成虚拟观测向量,以减少虚假成分。结合两者的优点,提出了基于EEMD特征增强的cICA的齿轮箱故障特征提取方法。通过仿真、试验与工程应用结果表明,该方法对齿轮箱单通道测量信号的低频故障特征提取具有很好的效果。(5)针对最小解熵解卷积(Minimum entropy deconvolution,MED)算法易受强噪声和野值的影响,引出了最大相关峭度解卷积(Maximum correlated kurtosis deconvolution,MCKD)的齿轮箱故障特征提取方法,克服了MED算法的不足。然而凭先验信息选取的故障周期,可能导致MCKD解卷积效果很差。因此提出了MCKD算法的最佳故障周期搜索思路,即在合适的滤波器阶数L下,故障周期的搜索可以在步距M取较大值时,限定于理论计算周期左右的某一范围内,使不同步距M关于最佳周期的最大相关峭度达到全局最优,以确保了MCKD算法具有良好的解卷积效果。通过试验齿轮箱和矿用齿轮箱的微弱低频故障特征提取佐证了最佳故障周期搜索思路的可行性和MCKD方法的有效性及优势。更多还原

【Abstract】 Gearbox,as a key part of mechanical equipment to transmit motion and power,is widely used in various huge and heavy mechanical equipments of modern industry,but its working and running condition is usually very poor.Once the unexpectedly failure occurred,the whole machine would not done well,increases equipment maintenance cost,and causes significant economic losses,or even catastrophic accidents.Therefore,the research of gearbox fault diagnosis technology and methods has important academic significance and application value.Fault feature extraction is a key problem for the gearbox condition monitoring and fault diagnosis,especially in the low-speed region,the effective fault information is very weak for the sake of interference from the high frequency gear meshing vibration,transmission path,strong noise,and so on,which brings many problems to the low-frequency fault feature extraction of gearbox.Therefore,It is necessary to find a suitable vibration signal processing technology and method to effectively separate the fault signal and extract the weak fault feature from the complex vibration signal.Considering gearbox steady-rotating working condition,new low-frequency fault feature extraction methods based on blind source separation(BSS)are researched in detail,and its applications for experimental gearbox and mine geabox are investigated.The main contents are as follows:(1)The background and significance of the selected topic are discussed,and the development of gearbox fault diagnosis methods based on time-frequency analysis and blind signal processing is introduced.According to the existed problems analysis of low-frequency fault feature extraction for gearbox based on BSS,the specific reasearch routes and contents of this paper are decided.(2)Taking into account source signal may be directly mixed with source noise,the statistical independence character and nongaussianity will be very weak,it leads to the difficult problem of fault information extraction with BSS.The source noise is added to the linear instantaneous mixture and convoluted mixture model,which is much more suitable for the real gearbox vibration system,and it is the base of the proposed new methods of low-frequency fault feature blind extraction for gearbox with source noise.(3)Constrained independent component analysis(cICA)algorithm has strong denosing ability for measured noise mixed in multi-channel measured signals,but very poor for source signal with source noise.Aiming at this problem,a method of gearbox fault feature extraction based on WT feature-enhanced and cICA is proposed.It canreduce the interference of strong noise and other vibration sources,improve signal-to-noise ratio(SNR),and enhance analysis effect of cICA algorithm.The proposed method is used to the fault diagnosis of test gearbox and mine belt conveyor gearbox,and low-frequency vibration feature is extracted from its vibration signal,respectively.(4)The number of observed signals is no less than that of source signals for cICA algorithm,and it cannot directly extract fault information from the single-channel measured signal.Ensemble empirical mode decomposition(EEMD)can effectively restrains mode aliasing,but with false components.The appropriate intrinsic mode functions(IMFs)are selected by computing the kurtousis and correlation coefficients,then constitute a new observed vector combined with the original signal.Merged the advantages of the two algorithms,a method of gearbox fault feature extraction based on EEMD feature-enhanced and cICA is proposed.By analyzing simulation,experiments and engineering application,the results show that the proposed method is effective for low-frequency fault feature extraction of single-channel measured signal of gearbox.(5)Minimun entropy deconvolution(MED)algorithm is unsuitable for strong noise and outliers,considering this problem,a method of fault feature extraction from gearbox based on maximum correlated kurtosis deconvolution(MCKD)is introduced,which can overcome the shortcomeing of MED algorithm.However,the effect of MCKD algorithm is probably poor according to priori information to select fault period.Therefore,an idea of fault period searching is discussed,the fault period can be limited to a certain range of computation period,and the maximum correlated kurtosis(KC)converges to the global maximum about the optimum fault period with large M and suitable L,and ensures the effective results for MCKD algorithm with different M.The results of weak and low-frequency fault feature extraction throught experiments and engineering applications for the mine gearboxes indicate that the feasibility of optimum fault period searching and the effectiveness and superiority of MCKD method is testified.更多还原