盲信号分离算法及其在转子故障信号分离中的应用方法研究
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摘要
在旋转机械设备状态监测和故障诊断研究中,故障的特征提取和模式识别关系到故障诊断的可靠性和准确性,也是旋转机械故障诊断研究中的关键问题。利用转子振动信号对其进行状态监测和诊断是目前旋转机械故障监测和诊断研究中常用的方法。本论文以丰富和提高机械故障诊断理论与方法为目的,用现代信号处理技术中的盲信号分离方法为工具,以机械设备中应用最广泛的旋转机械设备为研究对象,利用盲信号分离算法、中值滤波和盲信号分离相结合的方法、自适应粒子群优化的盲信号分离方法、降噪源分离方法等信号处理方法,对转子系统故障特征提取问题开展了研究工作。具体研究内容如下：
(1)针对噪声干扰下的旋转机械故障特征提取问题,提出一种基于二阶盲辨识的去除干扰噪声方法。该方法利用旋转机械振动信号的非平稳性特征,将采集到的信号分成不重叠的时间窗,然后对每个时间窗内的时滞方差平均值进行估计,从而实现噪声信号与源信号的分离。这里将盲信号分离理论应用于消噪处理,其关键是分离噪声,而不是滤除噪声,因此在分离噪声时不丢失有效信号,为消噪处理提供了一种新方法。此方法通过仿真和对实际转子振动数据的处理表明,该算法可有效地分离出干扰噪声,提高采样信号的准确性。
(2)针对非线性机械故障信号分离依赖于非线性函数的选取问题,提出一种基于自适应粒子群优化的机械故障特征提取方法。该方法将采样信号的负熵做为目标函数,然后引入自适应粒子群优化的概念,通过信号的状态自适应的调整惯性因子,使其负熵最大化,从而实现各振源信号的有效分离。仿真和试验结果表明,该方法提高了分离信号的相关系数,实现了各源信号的有效分离。
(3)提出了基于降噪源分离的旋转机械故障特征提取方法。该方法是根据旋转机械振动信号的统计特征,构造降噪函数,依据降噪函数实现各分量的分离。在对仿真故障信号实验的基础上,定量比较了四种降噪函数的性能,发现基于正切降噪函数的分离结果相似系数最好,更适于混叠故障信号的分离。将基于正切降噪函数的源分离方法应用于旋转机械故障特征提取中,分析结果表明,该方法很好地从转子混叠振动信号中分离出了转子由碰摩故障引起的转子不平衡和不对中故障。
(4)针对源信号分离算法对强脉冲噪声环境下的混叠振动信号分离的失效,构建了一种基于中值滤波和盲信号分离算法相结合的方法。该方法首先通过中值滤波降噪方法对振动信号进行降噪处理,然后通过盲信号分离算法对降噪后的混叠信号进行分离。仿真和实验结果表明：在强脉冲噪声干扰下,若直接采用盲信号分离算法进行分离,其分离效果并不理想,若利用中值消噪和盲信号分离算法相结合的方法,则分离效果得到明显提升。
Fault feature extraction and pattern recognition is the most crucial problem for the reliability and accuracy in the fault diagnosis of rotating machineries. This dissertation addresses the fault diagnosis of rotating machineries, with the purpose of enriching machine fault diagnostics and requirements of engineering application of fault diagnosis of the key equipment in mechanical engineering, by means of constrained blind source separation methods. It is necessary and important to diagnose machine fault accurately and effectively, so as to provide maintenance strategy and deduce economic losses. It is not only of great theoretical significance, but also of great engineering value. This dissertation explores the applications of the theories with second order blind separation, median filtering, adaptive particle swarm optimization, denoising source separation in the feature extraction, representation and vibration signal for the rotary machinery. The main research works can be described as follows:
(1) Noise reduction usually is conducted before analysis of mechanical fault feature, which could damage effective signals.This article proposes an algorithm of blind source separation based on the second-order statictics.The method focuses on noise separation rather than noise removal.So there are no harms to effective signals. This idea might provide a new way for noise reduction. The algorithm of blind source separation based on the second-order statistics blind identification is applied to mechanical vibration data.The results show that the algorithm is effect,noises are separated and re-moved, and accurate the rotor fault feature are picked up.
(2) The performance of existing nonlinear mechanical failure signal separation methods is affected by the non-linear contrast function that is selected according to the distribution of original signals. To solve this problem, a blind source separation algorithm based on adaptive particle swarm optimization is proposed, which takes the negentropy of mixtures as a contrast function. The inertia weight factor depends on the negentropy, which can improve the contradiction between the convergence speed and the performance of separated signals. The simulation results was verified the effectiveness of the proposed method. Finally, Some mixed rotor vibration signals were separated successfully using the proposed method.
(3) Signal processing methods are commonly used to analyze the structure of signals according to the criteria of spectral distribution. However, the causal relationship between components and sources are not revealed. Under the condition that only observed signals are known, the mixed signals can be separated into several components by denoising source separation (DSS) method according to statistical feature. The sources of observed signals are revealed by these independent components, thus it provides a direct reference to condition monitoring and active control of vibration and noise. The basic theory of DSS and denoising functions based on different criterion are studied, and the separation performance of four types of denoising function such as energy function, slope function, kurtosis function and tangent function are quantitatively compared by means of simulation of typical mechanical signals. The results show that the algorithm based on tangent function is more suitable for extracting nonlinear coupling information of mechanical equipment. The DSS method based on tangent function is used to extract running information feature of rotor, and the quantitative analysis results show that some mixed rotor vibration signals were separated successfully using the proposed method.
(4) When the rotary machinery is running, the vibration signals measured with sensors are mixed with all vibration sources and contain very strong noises. It's difficult to separate mixed signals with conventional methods of signal processing, so there are difficulties in machine health monitoring and fault diagnosis. The principle and method of blind source separation were introduced here, and it was pointed out that the blind source separation algorithm was invalid in strong pulse noise environment. For the vibration signals in strong pulse noise environment, they were de-noised with the median filter method firstly, and then the de-noised signal was separated with the blind source separation algorithm. The simulation results was verified the effectiveness of the proposed method. Finally, Some mixed rotor vibration signals were using the proposed method. Thus, a new separation approach for vibration signals in strong pulse noise environment was provided.
(1)针对噪声干扰下的旋转机械故障特征提取问题,提出一种基于二阶盲辨识的去除干扰噪声方法。该方法利用旋转机械振动信号的非平稳性特征,将采集到的信号分成不重叠的时间窗,然后对每个时间窗内的时滞方差平均值进行估计,从而实现噪声信号与源信号的分离。这里将盲信号分离理论应用于消噪处理,其关键是分离噪声,而不是滤除噪声,因此在分离噪声时不丢失有效信号,为消噪处理提供了一种新方法。此方法通过仿真和对实际转子振动数据的处理表明,该算法可有效地分离出干扰噪声,提高采样信号的准确性。
(2)针对非线性机械故障信号分离依赖于非线性函数的选取问题,提出一种基于自适应粒子群优化的机械故障特征提取方法。该方法将采样信号的负熵做为目标函数,然后引入自适应粒子群优化的概念,通过信号的状态自适应的调整惯性因子,使其负熵最大化,从而实现各振源信号的有效分离。仿真和试验结果表明,该方法提高了分离信号的相关系数,实现了各源信号的有效分离。
(3)提出了基于降噪源分离的旋转机械故障特征提取方法。该方法是根据旋转机械振动信号的统计特征,构造降噪函数,依据降噪函数实现各分量的分离。在对仿真故障信号实验的基础上,定量比较了四种降噪函数的性能,发现基于正切降噪函数的分离结果相似系数最好,更适于混叠故障信号的分离。将基于正切降噪函数的源分离方法应用于旋转机械故障特征提取中,分析结果表明,该方法很好地从转子混叠振动信号中分离出了转子由碰摩故障引起的转子不平衡和不对中故障。
(4)针对源信号分离算法对强脉冲噪声环境下的混叠振动信号分离的失效,构建了一种基于中值滤波和盲信号分离算法相结合的方法。该方法首先通过中值滤波降噪方法对振动信号进行降噪处理,然后通过盲信号分离算法对降噪后的混叠信号进行分离。仿真和实验结果表明：在强脉冲噪声干扰下,若直接采用盲信号分离算法进行分离,其分离效果并不理想,若利用中值消噪和盲信号分离算法相结合的方法,则分离效果得到明显提升。
Fault feature extraction and pattern recognition is the most crucial problem for the reliability and accuracy in the fault diagnosis of rotating machineries. This dissertation addresses the fault diagnosis of rotating machineries, with the purpose of enriching machine fault diagnostics and requirements of engineering application of fault diagnosis of the key equipment in mechanical engineering, by means of constrained blind source separation methods. It is necessary and important to diagnose machine fault accurately and effectively, so as to provide maintenance strategy and deduce economic losses. It is not only of great theoretical significance, but also of great engineering value. This dissertation explores the applications of the theories with second order blind separation, median filtering, adaptive particle swarm optimization, denoising source separation in the feature extraction, representation and vibration signal for the rotary machinery. The main research works can be described as follows:
(1) Noise reduction usually is conducted before analysis of mechanical fault feature, which could damage effective signals.This article proposes an algorithm of blind source separation based on the second-order statictics.The method focuses on noise separation rather than noise removal.So there are no harms to effective signals. This idea might provide a new way for noise reduction. The algorithm of blind source separation based on the second-order statistics blind identification is applied to mechanical vibration data.The results show that the algorithm is effect,noises are separated and re-moved, and accurate the rotor fault feature are picked up.
(2) The performance of existing nonlinear mechanical failure signal separation methods is affected by the non-linear contrast function that is selected according to the distribution of original signals. To solve this problem, a blind source separation algorithm based on adaptive particle swarm optimization is proposed, which takes the negentropy of mixtures as a contrast function. The inertia weight factor depends on the negentropy, which can improve the contradiction between the convergence speed and the performance of separated signals. The simulation results was verified the effectiveness of the proposed method. Finally, Some mixed rotor vibration signals were separated successfully using the proposed method.
(3) Signal processing methods are commonly used to analyze the structure of signals according to the criteria of spectral distribution. However, the causal relationship between components and sources are not revealed. Under the condition that only observed signals are known, the mixed signals can be separated into several components by denoising source separation (DSS) method according to statistical feature. The sources of observed signals are revealed by these independent components, thus it provides a direct reference to condition monitoring and active control of vibration and noise. The basic theory of DSS and denoising functions based on different criterion are studied, and the separation performance of four types of denoising function such as energy function, slope function, kurtosis function and tangent function are quantitatively compared by means of simulation of typical mechanical signals. The results show that the algorithm based on tangent function is more suitable for extracting nonlinear coupling information of mechanical equipment. The DSS method based on tangent function is used to extract running information feature of rotor, and the quantitative analysis results show that some mixed rotor vibration signals were separated successfully using the proposed method.
(4) When the rotary machinery is running, the vibration signals measured with sensors are mixed with all vibration sources and contain very strong noises. It's difficult to separate mixed signals with conventional methods of signal processing, so there are difficulties in machine health monitoring and fault diagnosis. The principle and method of blind source separation were introduced here, and it was pointed out that the blind source separation algorithm was invalid in strong pulse noise environment. For the vibration signals in strong pulse noise environment, they were de-noised with the median filter method firstly, and then the de-noised signal was separated with the blind source separation algorithm. The simulation results was verified the effectiveness of the proposed method. Finally, Some mixed rotor vibration signals were using the proposed method. Thus, a new separation approach for vibration signals in strong pulse noise environment was provided.
引文
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