[医药]近似熵应用.doc

摘 要 本次毕业设计的目的是利用谐波小波与近似熵两种方法对含噪声的振动信号进行分析，最终达到区分有噪和无噪振动信号的目的。 近似熵是一个从衡量时间序列复杂性的角度出发的反映信号整体特征的指标,其具有计算所需数据短,对确定性信号和随机信号都有效的特点。本文在第一部分着重介绍了近似熵的概念、性质及其快速算法,其后引用实例并进行编程实验分析，从结果显示,近似熵在分析复杂的信号特征方面具有很强的能力。 由于现有的信号分析与处理的方法在高频段细化分析以及对非平稳信号和奇异信号的分析方面不理想。为解决这个问题，必须进行新的信号分析与处理方法的研究，以便对故障信号进行分析。本文第二部分所介绍的是以谐波小波和复morlet小波为主的用复小波方法分析与处理故障信号的新的故障信号处理方法。包括对谐波小波以及复morlet小波概念及性质的介绍，从小波的频谱出发对具有严格盒形谱特性及简单的解析表达式的谐波小波的运用，并经过严格的数学推导，得到了基于FFT的谐波小波算法，最后通过引用实际实例和相关编程实验表明,以复morlet小波在提取故障信号的特征方面同样具有很强的能力。 关键词：近似熵，谐波小波，复morlet小波，噪声信号分析 An Analysis Of The Noises Signal Using Approximate Entropy AND Harmonic Wavelet ABSTRACT The purpose of this graduation project is to use Approximate Entropy and Harmonic Wavelet to analyse the vibration signal contained noises , and to distinguish whether the vibration signal is contained nosies or not. Approximate entropy is a measure of time series complexity from the perspective of reflecting the overall characteristics of the target signal, the time of calculating the data is short,and,it is effectual to both signal and application of random signal characteristics. The first part of this article introduces the approximate entropy concept, nature and rapid algorithms.By programming and quoting examples, it is strong of the approximate entropy capacity in the analysis of the complexity of signals . Because it is unsatisfactory that the existing signal analysis and processing methods analyse high-frequency bands and the detailed of non-smooth signals and strange signals . To solve this problem,it needs an approach to signal analysis and research in order to analyse the signal containing failure. The second part of this article introduces a new approach to analyzing signal failures and resolves wavelet of morlet wavelet-based analysis and processing methods used to wavelet failure signals. Including harmonics wavelet morlet wavelet and the concept and nature of the presentation, as well as the spectrum starting from wavelet, a strict construction of a box-s