【作者】 张进；

【导师】 褚福磊；

【作者基本信息】 清华大学 ， 机械工程， 2010， 硕士

【摘要】 振动信号分析是对旋转机械进行故障诊断的最主要途径,由于受各种因素影响,如工况变化和设备自身故障等,振动信号中一般都含有非平稳成分。这些非平稳成分往往都包含有丰富的故障信息,因此,对这部分信号分析就显得非常重要。在许多分析方法中,Fourier变换是使用最为普遍也最为成熟的一种,遗憾的是它对非平稳信号的分析能力有限,不能很好地揭示非平稳信号所包含的信息。短时Fourier变换的出现为解决这个问题提供了一个好的开始,但由于其基函数的变换窗口固定不变,在一定程度上限制了非平稳信号的分析。相比较而言,小波变换在非平稳信号分析中具有独特优点。本论文的主要内容是在小波变换理论基础上,提出时间-小波能量谱及交叉小波变换两种信号处理方法,并将它们分别用于齿轮、滚动轴承故障诊断及水轮机振动信号分析。冲击信号发生的频率信息是诊断齿轮、滚动轴承故障的重要依据之一,通过对齿轮、滚动轴承等故障机理研究,在小波变换理论的基础之上提出了时间-小波能量谱分析方法。运用时间-小波能量谱对缺齿、磨损等齿轮故障信号及外圈、内圈、滚珠等滚动轴承故障信号进行分析,并将分析结果与传统的包络解调分析及Hilbert-Huang变换的结果进行对比。结果表明:时间-小波能量频谱能够有效的提取出其他分析方法无法提取的故障特性信息,在齿轮、滚动轴承等故障诊断中取得了很好的分析结果。水压脉动是影响水力发电机组振动的主要因素之一,分析水压脉动与机组振动之间的相关性对于研究水压脉动对机组振动的影响机理具有重要意义。基于小波变换理论的交叉小波变换是一种在时频空间中分析两个信号相关性的分析方法。论文中将交叉小波变换用于分析水轮机导轴承振动和尾水管出口、蜗壳进口、顶盖下等三处典型水压脉动之间的相关性,并将分析结果与传统的互相关分析进行对比。结果表明:交叉小波变换能够提取出不同水压脉动对机组振动的影响的频率成分及时间信息,而互相关分析无法达到这样的分析效果。更多还原

【Abstract】 Fault diagnostics is useful for ensuring the safe running of rotating machines and vibration signal analysis has been widely used for fault diagnostics. Various kinds of factors, such as the change of the environment and the faults from the machine itself, often make the vibration signal of the running machine contain non-stationary components. So it is important to analyze the non-stationary signals. Among many signal processing methods, the most common tool utilized in real-signal applications is the Fourier transform(FT) which decomposes a given signal into its frequency components. Unfortunately, this technique requires that a signal to be examined is stationary, i.e. without time-evolution of the frequency content. FT-based methods are not suitable for non-stationary signal analysis, with an intermittent and changing frequency pattern. The limitation of the Fourier analysis can be partly resolved by using a short-time Fourier transform(STFT). One critical limitation of the STFT appears when windowing the signal mainly due to the violation of the uncertainty principle. More precisely, if the window is too narrow, the frequency resolution will be poor, whereas if the window is too wide, the time localization will be less precise. So STFT is not suitable for analyzing signals involving different scales or range of frequencies. Compared with the STFT, the wavelet transform has many distinct advantages for vibration signal analysis. Time-wavelet Spectrum analysis and Cross-wavelet Transform based on the wavelet transform theory are brought out. The main aim of the present dissertation is to extract the feature information of the gear and bearing fault by using time-wavelet spectrum analysis and analyze the vibration signal of hydraulic turbine by using cross-wavelet transform.The impulses in vibration signals and their spectral features are important in diagnosing localized damage of gear and bearing. A new method, so called time-wavelet energy spectrum which is based on the theory of wavelet transform, is proposed for gear and bearing fault diagnosis. It can extract the feature of impulses in both time domain and frequency domain. It is applied to analyze the vibration signals of a gearbox under worn and broken statuses and bearing with outer ring fault, inner ring fault and ball fault. Envelope-demodulation analysis and Hilbert-Huang tranform are also used to analyze those signals. The result shows that the time-wavelet energy spectrum is more effective in extracting the impulse features produced by gear and bearing damage than other methods of signal processing.Hydaulic pressure fluctuation is one of major factors influencing the vibration of hydraulic turbines. Correlation analysis of the hydaulic pressure fluctuation and the turbine vibration is important to reveal the hydaulic pressure fluctuation induced vibration. Cross-wavelet transform based on the wavelet theory is used to analyze the two signals’correlation in time-frequency domain. In this dissertation, cross-wavelet transform is used to analyze the vibration at water turbine guide bearing and the hydaulic pressure fluctuation at draft tube, spiral case and headcover in time-frequency joint domain. The time-frequency correlation between the vibration and the hydaulic pressure fluctuation are extracted. The traditional cross-correlation analysis is also used to analyze those signals. The result shows that cross-wavelet transform can extract not only the time information but also the frequency information of the correlation of two signals, which the traditional cross-correlation analysis cannot.更多还原