往复滑动摩擦副磨合过程摩擦振动非线性特征研究

发布时间：2018-03-05 00:00

本文选题：摩擦振动　切入点：谐波小波包　出处：《大连海事大学》2015年博士论文　论文类型：学位论文

【摘要】：磨损是相互接触的物体在相对运动中表层材料不断损伤的过程,机器设备运转过程中摩擦副的相对运动会产生磨损。磨损是造成零件失效和材料损耗的主要原因,60-80%机械零件的失效是由磨损引起的,世界工业化发达国家能源约30%以不同形式的磨损消耗。因此,监测机械设备的摩擦磨损状态,识别设备早期磨损故障对于机器的节能减耗、合理运行及安全生产均具有重要的意义。摩擦振动是机械设备运转过程中摩擦副摩擦磨损产生的一种现象,蕴涵着反映摩擦副摩擦磨损状态的信息,可实时在线采集而不影响设备正常运行。因此,开展摩擦副摩擦磨损过程中摩擦振动的研究,准确提取摩擦振动特征,可为机械设备磨损状态监测及故障诊断提供理论依据和方法。摩擦振动信号能够反映摩擦副的磨损状态,但是含有噪声的摩擦振动信号不能真实反映摩擦副的磨合磨损状态。本文以在磨合磨损试验中采集的振动信号为研究对象,采用谐波小波包变换理论,结合摩擦系数与磨损表面,探讨了摩擦振动信号的降噪方法。结果表明：谐波小波包变换的方法可实现摩擦振动信号的提取。在摩擦振动信号降噪研究的基础上,通过磨合磨损试验研究了摩擦振动的时域特性和频域特性。(1)时域特性研究表明：摩擦振动信号是微弱信号,具有幅值小能量低的特点；随着磨合过程的进行,摩擦振动信号的振幅呈减小趋势。载荷、润滑介质和材质影响摩擦振动信号振幅的变化,随着试验载荷的增加,摩擦振动信号的振幅增大；亦均不相同；初始表面对磨合初期的摩擦振动信号的振幅影响明显,随着磨合的进行,这种影响逐渐减小。(2)频域特性研究表明：摩擦振动信号的频谱中以边频为主,没有明显固有频率出现,摩擦振动信号具有非线性特征。摩擦振动激发的信号的频率分布范围与摩擦副材质有关,而与载荷、润滑介质及初始表面等试验条件无关。本文试验结果表明高熵合金材质的摩擦副摩擦振动信号频率分布范围在2000-3000Hz；铸铁材质的摩擦副摩擦振动信号频率分布范围在2000-3000Hz；中碳钢材质的摩擦副摩擦振动信号频率分布范围在3000-4000Hz。摩擦磨损过程是极其复杂的非线性动力学过程,混沌理论是非线性问题研究的有效手段,本文在摩擦振动信号特性研究的基础上,应用混沌理论研究了摩擦振动信号的非线性动力学特征。采用最大Lyapunov指数法、赝相空间法、功率谱方法、Poincare截面法及频闪采样法分析了摩擦振动信号混沌的动力学特征；计算了摩擦振动信号的混沌特征参数关联维和K熵,提取了摩擦振动信号混沌吸引子；通过试验探讨了摩擦副在不同磨损状态下摩擦振动信号混沌吸引子在相空间中的演变历程,结果表明：摩擦振动信号吸引子在相空间中具有一定的层次和结构,功率谱为类似噪声的宽峰谱,摩擦振动信号的Poincare截面图和频闪图均具有精细的层次和结构,其主分量谱为过定点且斜率为负的直线,证明了摩擦振动号具有混沌的动力学特征；在摩擦副磨合磨损过程中,摩擦振动信号混沌吸引子在相空间中的体积呈现先收缩然后稳定的趋势,反映了摩擦副从磨合磨损到稳定磨损的动力学过程。
[Abstract]:Wear is a process of mutual contact objects in relative motion in the surface materials to damage, the friction of the relative motion pair will produce wear during the running machine. The wear is mainly caused by the failure and the loss of material failure, 60-80% machine parts is caused by wear, the world developed countries about 30% energy consumption different forms of wear. Therefore, friction and wear condition monitoring of machinery and equipment, equipment identification of wear fault early for a machine to reduce energy consumption, rational operation and production safety are of great significance. The friction vibration is a friction phenomenon of friction and wear of mechanical equipment in the operation process, contains information reflecting the friction friction and wear the real-time online collection without affecting the normal operation of the equipment. Therefore, to carry out research on Friction Vibration and friction friction and wear process, accurate extraction The friction vibration characteristics, can be a mechanical equipment wear condition monitoring and fault diagnosis and provide a theoretical basis and methods. The friction vibration signal can reflect the wear state of the friction pairs, but the friction vibration signal with noise can not reflect the true state of the side friction wear. In this paper, the vibration signal collected in the trial running in wear as the research object, using the harmonic wavelet packet transform theory, combined with the friction coefficient and wear surface, discusses the method of noise reduction of friction vibration signals. The results show that the method of harmonic wavelet packet transform can realize the extraction of friction vibration signals. Based on the study of friction vibration signal in time domain and frequency domain characteristics of friction vibration tests are carried out to study the wear. (1) characteristics of the time domain shows that friction vibration signal is a weak signal, has the characteristics of small amplitude low energy; with the running in process The amplitude of vibration signal, the friction decreases. Load, lubrication medium and material influence the change of friction vibration signal amplitude, with the increase of the load, the friction vibration signal amplitude increases; also are not the same; the amplitude of the vibration signal of the initial surface friction on the run in period significantly, with the running of the influence decreases. (2) study on frequency characteristics showed that the spectrum of friction vibration signal in frequency, no obvious inherent frequency, the friction vibration signal has nonlinear characteristics. The friction vibration excitation signal frequency distribution and friction material, and load independent lubrication medium and initial surface test in this paper. The test results show that high entropy alloy material of the friction vibration signal frequency distribution in the range of 2000-3000Hz; the friction vibration signal of cast iron The frequency distribution in the range of 2000-3000Hz; in carbon steel friction vibration signal frequency distribution in the range of 3000-4000Hz. friction and wear process is a nonlinear dynamics process is extremely complex, chaos theory is an effective method to study the nonlinear problems, based on the characteristics of research on friction vibration signal, the nonlinear dynamic characteristics of friction vibration signal is applied to the research on the chaos theory. Using the maximum Lyapunov index method, the pseudo phase space method, the method of power spectrum, Poincare section method and stroboscopic sampling analysis of dynamic friction characteristic of chaotic vibration signal; chaotic characteristic parameter correlation dimension and K entropy to calculate the friction vibration signal, extraction of chaotic attractors of friction vibration signals; the effect of evolution. The friction pair under different wear conditions of friction vibration signal of chaotic attractor in phase space. The results showed that the friction Wipe the vibration signal of the level and structure of the attractors in phase space, the power spectrum is wide peak noise like spectrum, friction vibration signals Poincare section and stroboscopic patterns are the level and structure of the fine, the principal component analysis for point and line with negative slope, proved, with dynamic friction vibration the character of chaos; in the friction wear side process, friction vibration signal of chaotic attractor in the phase space volume showed the first contraction and then a stable trend, reflecting the friction from wear to wear. The dynamic process of stability

【学位授予单位】：大连海事大学
【学位级别】：博士
【学位授予年份】：2015
【分类号】：TH117

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