基于matlab及小波变换的非平稳随机信号的消噪处理与分析.doc
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基于matlab及小波变换的非平稳随机信号的消噪处理与分析,基于matlab及小波变换的非平稳随机信号的消噪处理与分析1.3万字自己原创的毕业论文，仅在本站独家出售，重复率低，推荐下载使用摘要：滚动轴承是旋转机械中应用最广泛的机械零件，也是最易损坏的元件之一。旋转机械的很多故障都与滚动轴承有关，轴承工作的好坏对机械的工作状态有很大的影响，所以对轴承振荡信号的故障诊断尤为重要，而...
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基于MATLAB及小波变换的非平稳随机信号的消噪处理与分析
1.3万字
自己原创的毕业论文，仅在本站独家出售，重复率低，推荐下载使用
摘要：滚动轴承是旋转机械中应用最广泛的机械零件，也是最易损坏的元件之一。旋转机械的很多故障都与滚动轴承有关，轴承工作的好坏对机械的工作状态有很大的影响，所以对轴承振荡信号的故障诊断尤为重要，而对滚动轴承信号的消噪处理就是故障诊断的第一步，也是很关键的一步，消噪的好坏对下一步的特征值提取和故障信号分类有着很大的影响。
本文在MATLAB软件平台上，利用滚动轴承正常情况下的信号与高斯白噪声信号叠加生成带噪信号，用小波变换对带噪信号进行了强制、默认阈值、给定软阈值、给定硬阈值、自适应阈值硬阈值、自适应阈值软阈值、启发式阈值硬阈值、启发式阈值软阈值、阈值等于sqrt(2*log(length(x)))硬阈值、阈值等于sqrt(2*log(length(x)))软阈值、用极大极小原理选择阈值的硬阈值、用极大极小原理选择阈值的软阈值消噪、小波包硬消噪、小波包软消噪总共十四种小波消噪方法来消噪，并提取滚动轴承正常情况下信号的均值和方差以及十四种消噪后的各自信号的均值和方差，将消噪后的信号的特征值与滚动轴承正常情况下的信号的特征值进行比较，观察哪种更接近正常情况下信号的特征值，说明哪种消噪方法更好。同时本文还提取了滚动轴承正常情况下消噪后的信号，内圈消噪后信号，外圈消噪后信号的特征值（均值，方差），并画出这三种信号消噪后功率谱密度图，结合均值，方差，功率谱密度图判断出了其中任何一个信号的故障类型。
通过特征值的比较发现启发式阈值硬阈值消噪方法在对带噪信号的消噪方法中是最好的方法，然后利用这种方法再对滚动轴承正常情况下的信号，内圈故障信号，外圈故障信号进行消噪，再对三种消噪后的信号进行特征值的提取，比较三种消噪后信号的特征值，发现均值，方差都可以用来区分这三种信号，最后生成三种消噪后信号的功率谱密度图，比较三者的功率谱密度图发现也可以通过功率谱密度图区分这三种信号，因此本文为大家提供了十四种信号的消噪方法，还提供了三种滚动轴承故障诊断方法。
关键词：非平稳随机信号 滚动轴承信号 小波变换 消噪 MATLAB软件
Based on MATLAB wavelet transform de-noising non-stationary random signal processing
Abstract Rolling is the widely used in rotating machinery mechanical parts, which is one of the most vulnerable components. Many failures of rotating machinery are concerned with rolling bearings, bearing good and bad work has a great influence on the mechanical working condition, which can lead to defective equipment generates abnormal vibration and noise, and even cause damage to the equipment, so the oscillation signal bearing fault diagnosis particularly important, while the noise canceling signal processing is the first step in rolling bearing fault diagnosis, but also a very crucial step, noise-canceling feature is good or bad value for the next fault signal extraction and final classification has a great impact.
In this paper, the MATLAB software platform, using the signal with a Gaussian white noise signal superimposed to generate noisy signals of rolling bearings under normal circumstances, using wavelet transform noisy signal forced default threshold, given the soft threshold, given the hard threshold, since Adaptive Threshold hard threshold, adaptive threshold soft threshold, the heuristic threshold hard threshold, the heuristic threshold soft threshold, the threshold is equal to sqrt (2 * log (length (x))) hard threshold, the threshold is equal to sqrt (2 * log (length ( x))) soft threshold, select the hard threshold threshold Minimax principle, choose soft threshold de-noising threshold Minimax theory, wavelet packet de-noising hard, soft wavelet packet wavelet de-noising total of fourteen kinds of consumer noise method to eliminate noise and extract the mean and variance of the mean and variance of the signal and noise cancellation after fourteen respective signals of rolling bearings under normal circumstances, the value of the characteristic features of the signal after de-noising and Rolling signal under normal circumstances value, which is more close to the characteristic values observed under normal circumstances, signal, indicating what kind of de-noising method is better. It also extracts the signal while the rolling bearing under normal circumstances, the signal characteristic value of the inner and outer signal (mean and variance), and draw the power spectral density of the three signals, combining the mean, variance, power spectral density determine which type of failure of any one of the signal.
Eigenvalues found by comparing the heuristic threshold hard threshold de-noising method for noisy signal de-noising method is the best method, and then use this method and then the signals of rolling bearings under normal circumstances, the fault signal inner and outer de-noising ring fault signal, then the signal is normal rolling bearings, the inner ring fault signal extracting outer fault signal characteristic value, comparison of three characteristic values of the original signal and found the mean, the variance can be used to distinguish between these three signal, and finally generate the power spectral density of three of the original signal, comparing the three power spectral density can be found in..
1.3万字
自己原创的毕业论文，仅在本站独家出售，重复率低，推荐下载使用
摘要：滚动轴承是旋转机械中应用最广泛的机械零件，也是最易损坏的元件之一。旋转机械的很多故障都与滚动轴承有关，轴承工作的好坏对机械的工作状态有很大的影响，所以对轴承振荡信号的故障诊断尤为重要，而对滚动轴承信号的消噪处理就是故障诊断的第一步，也是很关键的一步，消噪的好坏对下一步的特征值提取和故障信号分类有着很大的影响。
本文在MATLAB软件平台上，利用滚动轴承正常情况下的信号与高斯白噪声信号叠加生成带噪信号，用小波变换对带噪信号进行了强制、默认阈值、给定软阈值、给定硬阈值、自适应阈值硬阈值、自适应阈值软阈值、启发式阈值硬阈值、启发式阈值软阈值、阈值等于sqrt(2*log(length(x)))硬阈值、阈值等于sqrt(2*log(length(x)))软阈值、用极大极小原理选择阈值的硬阈值、用极大极小原理选择阈值的软阈值消噪、小波包硬消噪、小波包软消噪总共十四种小波消噪方法来消噪，并提取滚动轴承正常情况下信号的均值和方差以及十四种消噪后的各自信号的均值和方差，将消噪后的信号的特征值与滚动轴承正常情况下的信号的特征值进行比较，观察哪种更接近正常情况下信号的特征值，说明哪种消噪方法更好。同时本文还提取了滚动轴承正常情况下消噪后的信号，内圈消噪后信号，外圈消噪后信号的特征值（均值，方差），并画出这三种信号消噪后功率谱密度图，结合均值，方差，功率谱密度图判断出了其中任何一个信号的故障类型。
通过特征值的比较发现启发式阈值硬阈值消噪方法在对带噪信号的消噪方法中是最好的方法，然后利用这种方法再对滚动轴承正常情况下的信号，内圈故障信号，外圈故障信号进行消噪，再对三种消噪后的信号进行特征值的提取，比较三种消噪后信号的特征值，发现均值，方差都可以用来区分这三种信号，最后生成三种消噪后信号的功率谱密度图，比较三者的功率谱密度图发现也可以通过功率谱密度图区分这三种信号，因此本文为大家提供了十四种信号的消噪方法，还提供了三种滚动轴承故障诊断方法。
关键词：非平稳随机信号 滚动轴承信号 小波变换 消噪 MATLAB软件
Based on MATLAB wavelet transform de-noising non-stationary random signal processing
Abstract Rolling is the widely used in rotating machinery mechanical parts, which is one of the most vulnerable components. Many failures of rotating machinery are concerned with rolling bearings, bearing good and bad work has a great influence on the mechanical working condition, which can lead to defective equipment generates abnormal vibration and noise, and even cause damage to the equipment, so the oscillation signal bearing fault diagnosis particularly important, while the noise canceling signal processing is the first step in rolling bearing fault diagnosis, but also a very crucial step, noise-canceling feature is good or bad value for the next fault signal extraction and final classification has a great impact.
In this paper, the MATLAB software platform, using the signal with a Gaussian white noise signal superimposed to generate noisy signals of rolling bearings under normal circumstances, using wavelet transform noisy signal forced default threshold, given the soft threshold, given the hard threshold, since Adaptive Threshold hard threshold, adaptive threshold soft threshold, the heuristic threshold hard threshold, the heuristic threshold soft threshold, the threshold is equal to sqrt (2 * log (length (x))) hard threshold, the threshold is equal to sqrt (2 * log (length ( x))) soft threshold, select the hard threshold threshold Minimax principle, choose soft threshold de-noising threshold Minimax theory, wavelet packet de-noising hard, soft wavelet packet wavelet de-noising total of fourteen kinds of consumer noise method to eliminate noise and extract the mean and variance of the mean and variance of the signal and noise cancellation after fourteen respective signals of rolling bearings under normal circumstances, the value of the characteristic features of the signal after de-noising and Rolling signal under normal circumstances value, which is more close to the characteristic values observed under normal circumstances, signal, indicating what kind of de-noising method is better. It also extracts the signal while the rolling bearing under normal circumstances, the signal characteristic value of the inner and outer signal (mean and variance), and draw the power spectral density of the three signals, combining the mean, variance, power spectral density determine which type of failure of any one of the signal.
Eigenvalues found by comparing the heuristic threshold hard threshold de-noising method for noisy signal de-noising method is the best method, and then use this method and then the signals of rolling bearings under normal circumstances, the fault signal inner and outer de-noising ring fault signal, then the signal is normal rolling bearings, the inner ring fault signal extracting outer fault signal characteristic value, comparison of three characteristic values of the original signal and found the mean, the variance can be used to distinguish between these three signal, and finally generate the power spectral density of three of the original signal, comparing the three power spectral density can be found in..
