【作者】 周晨赓；

【导师】 赵犁丰；

【作者基本信息】 中国海洋大学 ， 信号与信息处理， 2003， 硕士

【摘要】 本文首先回顾了一些时频分析方法，分析其具有的特点以及存在的不足，然后介绍了一种新的分析非线性、非平稳信号的方法，并将它与以往的时频分析方法进行对比，指出其具有的优势。在此基础之上，对将它与神经网络联合进行故障诊断进行了研究。 以往的大多数时频分析方法均是对传统的傅里叶分析进行修改后得到的，因此，傅里叶分析所存在的问题，如用谐波分量来表达信号的中的突变，是一个时间区间上的积分平均，或者是由传统的频率定义所带来的问题，如时间分辨率和频率分辨率的矛盾性，各种时频分析方法或多或少都存在，提高某一参数指标的同时往往是以牺牲另一参数指标为代价的。 本文介绍了一种新的分析非线性、非平稳信号的有力工具-EMD方法，与传统的对信号做积分变换的方法不同的是，它是将信号中不同尺度的波动或趋势逐级分解开来，产生一系列具有不同特征尺度的数据序列，每一个序列称为内在模函数。而各个内在模函数包含并突出了原信号的局部特征信息，这样通过对各内在模函数进行分析，便能够良好的把握原信号中所含有的特征信息量。实验证明，EMD也适用于线性平稳信号，这时候他分解出来的内在模函数就代表了原信号的各频率分量。事实上作为一种只是提取信号特征量的方法，EMD方法常常是与其他各种方法联合起来对信号进行分析。 用神经网络进行故障诊断实质上是一个模式识别的问题。其重点与难点在于如何有效的提取信号中的特征量，即分类标准的找寻问题。EMD方法的提出为解决这一问题提供了新的途径。本文通过将原信号用EMD分解而成的内在模函数输入到BP网络中进行训练学习并进行故障信号的判别进行实验，验证了EMD方法与BP网络的联合不仅能够大大提高判别的准确率，而且大大缩短了BP网络的学习收敛时间。本文的最后还对减少输入的内在模函数数量，简化网络结构进行了研究，证明其可行性。 在本文的结尾，展望了EMD在故障诊断领域的应用前景，指出了EMD方法还存在的不足之处以及未来的发展方向。更多还原

【Abstract】 In this thesis some Time-Frequency analyzing method are reviewed first, analyzing their advantages and disadvantages. Then a new analyzing nonlinear and non-stationary signal method is introduced. And a comparison between it and the other Time-Frequency analyzing method is done, pointing out the new method’s advantages. On this basis, using it associated with BP neural network in fault diagnosis is investigated thoroughly.Most of the former Time-Frequency analyzing methods are derived from the Fourier analysis. So they more or less have the problem exiting in Fourier analysis, for example: the discontinuity expressed with harmonic components, being integral mean of a time interval; and the problems derived from the conventional definition of frequency, that is, the contradiction between the time and frequency resolution. Improving the performance of some parameter is at the cost of sacrificing the other one.A powerful tool for analyzing nonlinear and non-stationary signal which is called EMD (empirical mode decomposition) method, is introduced. Different from the traditional method in doing integral transformation to signal, it decompose signal into several IMFs (intrinsic mode function), which contain and extrude the local characteristic of signal. So the characteristic information of the original signal can be well held by analyzing the IMFs. The experimentation testifies that the EMD method applies to linear and stationary signal. At this circumstance, the decomposed IMFs are the frequency components. In fact, EMD is a method which extract characteristic from signal. So in most circumstances, it is combined with other method to analyse signal.The essence of using neural network in fault diagnosis is a matter of pattern recognition. The emphasis and difficulty lie in how to extract characteristic of signal efficiently, that is, the problem of seeking classification criteria. The appearance of EMD method provides a new way to solve this problem. Fault diagnosis experimentation is carried out. In the experimentation the IMFs decomposed fromsignal are input into a designed BP neural network to train the network. Through this experimentation it is verified that the method of EMD in conjunction with BP neural network can not only improve the distinguishing accuracy, but also reduce the learning time of BP neural network greatly. What’s more, the study covers how to decrease the number of IMFs and simplify the structure of BP neural network. A experiment proves its feasibility.At the end of the thesis, the application prospect of EMD in fault diagnosis is revealed. The disadvantages and development direction of EMD are pointed out.更多还原