基于小波分析和神经网络的汽轮机故障诊断研究
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摘要
汽轮机组的诊断一直是故障诊断技术应用的一个重要方面。在众多常见故障的发生率中,振动故障占了总数的95%以上。基于这种考虑才选定了汽轮机故障诊断技术研究一题,尤其是探索如何智能预测和诊断转子振动故障。
本论文主要进行了基于小波分析的信号处理和基于神经网络的智能故障诊断两方面的理论上的研究工作。主要研究内容总结如下:
(1)本文研究了小波(包)母函数及基的选择问题。小波及小波包变换在故障诊断领域中有着广泛的应用,它帮助我们获得大量故障信号的特征信息。但是,面对大量的小波母函数以及变换后的很多小波包基,我们需要选择合适的小波母函数及其基,因为并非任意的小波母函数及任意的小波包基都是合适的。
(2)RBF网络训练的关键在于隐含层参数的确定。RBF网络目前已有的几种训练方法对于含有随机噪声的复杂样本训练速度过慢且分类性能不稳定。针对这些缺点,本文采用了改进的遗传算法——免疫遗传算法来优化RBF网络隐含层参数。同时,在训练过程中采用基于构造法的方法来寻找最佳的隐含层节点数。
(3)本文采用“小波包—能量”法来提取信号的特征量。小波包分析能有效地提取汽轮机振动信号中的有用成分,作为故障诊断的依据。针对强噪声背景的高频振动信号,提出了一种基于能量的自适应阈值选取算法。本方法对于诊断频率分布范围较广且信号具有较强时变性和复杂环境下的故障有着良好的应用前景。在故障诊断中的实践也验证了该方法的有效性。
Fault diagnosis of turbine is an important aspect of the fault diagnosis technology application. Among the incidence of many common faults, the vibration fault is account for more than 95%.Based on this consideration; I selected the subject on fault diagnosis technology that, in particular, to explore ways to predict and diagnose intelligently fault of rotor vibration.
The present paper has mainly carried on research works theoretically of two aspects about signal processing based on the wavelet analysis and intelligence failure diagnosis based on the neural network. The main research content summary is as follows:
(1)The selection of wavelet packet mother functions and their bases is discussed in this paper. There are vast application of wavelet transform and wavelet packet transform in the fault diagnosis fields. The transforms help us to obtain a number of feature information of fault signals. But in the face of a lot of mother function of wavelet transform and a number of bases after transform, we must select a proper mother function of wavelet transform and his bases, because not all mother functions and their bases are proper.
(2) The key to training of a radial basis function (RBF) network is to determine the parameters of hidden layers of the network. There are a number of training methods of RBF networks. But the shortcomings of the methods are that the training speeds are too slow and the ability to classify is unstable. In view of these shortcomings, this article uses the advanced genetic algorithm--immunity genetic algorithm to optimize the hidden layer parameters of RBF neural network. At the same time, we seek the best hidden layer units based on construction method in the training process.
(3) In this paper, "wavelet packet--energy" method is used to extract the characteristics of signals. Wavelet packet analysis can be effective in extracting the useful elements of turbine machine vibration signals as the basis for fault diagnosis. According to high-frequency vibration signals in the strong noise background, a new energy-based adaptive threshold selection algorithm is proposed. This method regarding the diagnosis frequency distribution range is broad when the signal has strong time variation and fault in the complex environment has the good application prospect. The experiments of fault diagnosis demonstrate that the method is valid.
本论文主要进行了基于小波分析的信号处理和基于神经网络的智能故障诊断两方面的理论上的研究工作。主要研究内容总结如下:
(1)本文研究了小波(包)母函数及基的选择问题。小波及小波包变换在故障诊断领域中有着广泛的应用,它帮助我们获得大量故障信号的特征信息。但是,面对大量的小波母函数以及变换后的很多小波包基,我们需要选择合适的小波母函数及其基,因为并非任意的小波母函数及任意的小波包基都是合适的。
(2)RBF网络训练的关键在于隐含层参数的确定。RBF网络目前已有的几种训练方法对于含有随机噪声的复杂样本训练速度过慢且分类性能不稳定。针对这些缺点,本文采用了改进的遗传算法——免疫遗传算法来优化RBF网络隐含层参数。同时,在训练过程中采用基于构造法的方法来寻找最佳的隐含层节点数。
(3)本文采用“小波包—能量”法来提取信号的特征量。小波包分析能有效地提取汽轮机振动信号中的有用成分,作为故障诊断的依据。针对强噪声背景的高频振动信号,提出了一种基于能量的自适应阈值选取算法。本方法对于诊断频率分布范围较广且信号具有较强时变性和复杂环境下的故障有着良好的应用前景。在故障诊断中的实践也验证了该方法的有效性。
Fault diagnosis of turbine is an important aspect of the fault diagnosis technology application. Among the incidence of many common faults, the vibration fault is account for more than 95%.Based on this consideration; I selected the subject on fault diagnosis technology that, in particular, to explore ways to predict and diagnose intelligently fault of rotor vibration.
The present paper has mainly carried on research works theoretically of two aspects about signal processing based on the wavelet analysis and intelligence failure diagnosis based on the neural network. The main research content summary is as follows:
(1)The selection of wavelet packet mother functions and their bases is discussed in this paper. There are vast application of wavelet transform and wavelet packet transform in the fault diagnosis fields. The transforms help us to obtain a number of feature information of fault signals. But in the face of a lot of mother function of wavelet transform and a number of bases after transform, we must select a proper mother function of wavelet transform and his bases, because not all mother functions and their bases are proper.
(2) The key to training of a radial basis function (RBF) network is to determine the parameters of hidden layers of the network. There are a number of training methods of RBF networks. But the shortcomings of the methods are that the training speeds are too slow and the ability to classify is unstable. In view of these shortcomings, this article uses the advanced genetic algorithm--immunity genetic algorithm to optimize the hidden layer parameters of RBF neural network. At the same time, we seek the best hidden layer units based on construction method in the training process.
(3) In this paper, "wavelet packet--energy" method is used to extract the characteristics of signals. Wavelet packet analysis can be effective in extracting the useful elements of turbine machine vibration signals as the basis for fault diagnosis. According to high-frequency vibration signals in the strong noise background, a new energy-based adaptive threshold selection algorithm is proposed. This method regarding the diagnosis frequency distribution range is broad when the signal has strong time variation and fault in the complex environment has the good application prospect. The experiments of fault diagnosis demonstrate that the method is valid.
引文
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