【作者】 李强；

【导师】 王太勇；

【作者基本信息】 天津大学 ， 机械制造及其自动化， 2005， 硕士

【摘要】 故障的发生和发展、设备工况的变化以及设备自身的非线性,使机械设备的动态信号表现出非平稳性。如果对非平稳信号进行有效的消噪、分解和解调,提取有用信息,抑制其他成分的干扰,将有助于提高信号的信噪比和诊断信息的质量。本课题以回转机械和往复机械为对象,研究了基于非平稳信号特征提取原理的多种故障诊断实用技术。针对绝热近似小参数随机共振难以满足工程实践中大参数下的弱信号检测,及当前研究主要集中于单一频率共振分析的问题,本文提出了一种新的变步长随机共振数值算法。该算法在大参数条件下可对淹没在强噪声中的多个弱信号产生共振输出。同时,作为小波分析的预处理,它可以有效抑制小波分析中由强噪声引起的边频干扰,提高小波分析在低信噪比信号检测中的可靠性。经验模式分解方法是一种新的非线性非平稳信号分析工具。该方法基于信号的局部特征,可以根据信号自身的时间特征尺度进行自适应分解,得到若干个基本模式分量。本文研究了基于经验模式分解的时频分析理论及算法。通过对分解得到的各基本模式分量进行Hilbert变换,就可以得到信号的瞬时频率,从而给出信号频率变化的精确表达。信号最终以Hilbert时频谱的形式表示为时频平面上的能量分布。旋转机械振动信号的非平稳性常表现为统计特性呈周期性的循环平稳。本文在对循环自相关解调及其性能分析的基础上,提出了基于经验模式分解的分频带循环自相关解调方法。该方法有效地抑制了多调制源和多载波对循环平稳结果带来的交叉项干扰,提高了分析的可靠性。将该方法应用于滚动轴承的故障诊断中,成功地从振动信号中分离出故障源。作为本课题关键技术的载体,本文总结了作者在开发基于LabVIEW的振动信号分析系统中运用的一些实用技术,提出了一种基于频域积分的振动参量转换修正算法,为设备动态信息的完整性和准确性提供了技术上的支持和保障。更多还原

【Abstract】 The dynamic signals of mechanical equipment often possess nonstationaritiesdue to occurrence of fault, variance of operation and inherent nonlinearity ofequipment. If the nonstationary signals are effectively denoised, decomposed anddemodulated, the useful components are extracted, and the interference from othercomponents are restrained, the signal to noise ratio and the quality of diagnosticinformation will be improved. Aiming at rotating machinery and reciprocatingmachinery, this research focuses on practical diagnosis techniques based on principleof nonstationary signal feature extraction.Aiming at the problems that adiabatic elimination stochastic resonance in smallparameters can not detect weak signal in large parameters in engineering applications,and present researches mostly focus on single frequency, a new numerical methodcalled the step-changed stochastic resonance is proposed in this thesis. The newmethod can detect the multi-frequency weak signal in large parameters which isoverwhelmed in heavy noise efficiently. And when it is used as the pretreatment of thewavelet analysis, it also can decrease the weak signals’ distortion induced by theheavy noise in wavelet analysis, and improve the reliability of the wavelet analysis inweak signal detection under the condition of low signal to noise ratio.Empirical mode decomposition (EMD) method is a new tool for the nonlinearand nonstationary signals analysis. It is based on the local feature of the signals, andcan adaptively decompose signals into several intrinsic mode functions (IMFs)according to its characteristic time scale. This thesis discusses the EMD-basedtime-frequency analysis method’s theory and basic algorithm. After the signal isdecomposed, the IMFs can be used to calculate the Hilbert spectrum to gain theinstantaneous frequency, and show the exactly changes of the frequency. Finally, thesignal is expressed by the energy distributing on the time-frequency plane in the formof Hilbert time-frequency spectrum.The nonstationarities of the vibration signals of rotating machinery usuallybehaves as its statistical feature is seasonal cyclostationary. Based on the analysis ofcyclic autocorrelation demodulation and its performance, the method of EMD-basedcyclic autocorrelation demodulation in specific frequency band is proposed. Themethod can decrease cross-term interference caused by multi-modulator andmulti-carrier and improve the reliability of the analysis. As the application of theEMD-based cyclic autocorrelation demodulation theory in fault diagnosis, the methodis used to extract fault feature from vibration signals of rolling element bearingsuccessfully.As the carrier of the key technology, some practical techniques of thedevelopment of the vibration signal analysis system based on LabVIEW aresummarized. A simple effective modification algorithm for the vibration signalsintegration in frequency domain is proposed. This algorithm provides the technicalsupport for the integrality and accuracy of equipment dynamic information.更多还原