基于能量掩膜信号法的连采机振动信号特征提取研究
发布时间：2018-10-19 18:57
【摘要】：在实际工程应用中,获得的信号一般为非平稳信号,对于非平稳信号的分析与处理十分重要。在处理这些数据序列时,以往经常用到的传统时频分析方法的根本都是傅里叶变换,因此在处理非平稳信号时会存在一定的局限性。经验模态分解(EMD)是美国国家宇航局的华裔科学家Norden E.Huang等人于1998年在分析非平稳、非线性信号时提出的一种新型的、具有自适应性的时频分析方法,在传统的信号处理方法上取得了很大的改进,是一种现代化的信号处理方法,并且它不需要任何先验知识,仅根据信号自身的特点自适应的将任一复杂非平稳信号分解为若干个内禀模态分量(IMF)和一个余量之和,所有的内禀模态分量经傅里叶变换之后都能够得到原信号的具有物理意义的瞬时频率。EMD方法相较于传统信号处理方法具有更多的优点,被广泛应用到图形处理、信号处理、振动测试和机械故障诊断等多个领域,都取得了良好的效果。本文在深入学习、研究EMD算法的基础上,对其存在的模态混叠现象进行了改进,提出了一种基于能量的掩膜信号法。根据能量守恒定律,当内禀模态分量中不存在虚假模态分量时,分解过程能量守恒,所有分量的能量之和等于原信号的能量,但是当有虚假模态分量存在时,能量是不守恒的,原信号的能量低于各分量的能量之和,任意两个分量之和的能量也是小于其能量之和的,以此确定了能量泄露的主要去向,降低了能量泄露对计算掩膜信号频率的影响,弥补了掩膜信号法的不足并将改进后的EMD算法应用在实际工程中的非平稳信号处理上。连续采煤机是大型的地下采掘设备,主要振动信号频率为低频,以连采机截割臂振动信号这一非平稳信号为例来进行研究。首先通过计算在连采机截割臂上不同点模态运动能的大小,从而对传感器的安装位置进行优化,得到了连采机截割臂在工作过程中的振动信号,在原信号中会存在噪声,通过EMD方法进行降噪,去掉信号中的高频噪声;然后通过改进的掩膜信号法对降噪后的信号进行分析研究,结果中成功消除了EMD中存在的模态混叠现象,说明了能量掩膜信号法在实际工程应用中也能达到消除模态混叠现象上的效果。
[Abstract]:In practical engineering applications, the obtained signals are generally non-stationary signals, which is very important for the analysis and processing of non-stationary signals. In the processing of these data sequences, the traditional time-frequency analysis methods often used in the past are based on Fourier transform, so there are some limitations in the processing of non-stationary signals. Empirical mode decomposition (EMD) is a new adaptive time-frequency analysis method proposed by Norden E.Huang et al., a Chinese scientist from NASA, in 1998 when analyzing nonstationary and nonlinear signals. Great improvement has been made in the traditional signal processing method, which is a modern signal processing method, and it does not require any prior knowledge. According to the characteristics of the signal itself, any complex non-stationary signal is decomposed into the sum of several intrinsic modal components (IMF) and a residue. After Fourier transform, all intrinsic modal components can obtain the physical instantaneous frequency of the original signal. Compared with the traditional signal processing method, the EMD method has more advantages and is widely used in graphic processing and signal processing. Many fields such as vibration test and mechanical fault diagnosis have achieved good results. In this paper, based on the study of the EMD algorithm, the existing mode aliasing is improved, and an energy-based mask signal method is proposed. According to the conservation law of energy, when there is no false mode component in intrinsic mode component, the energy conservation of decomposition process, the sum of energy of all components is equal to the energy of the original signal, but when there is false mode component, the energy is not conserved. The energy of the original signal is lower than the sum of the energy of each component, and the energy of the sum of any two components is also smaller than the sum of its energy. The main direction of the energy leakage is determined, and the influence of the energy leakage on the calculation of the frequency of the mask signal is reduced. It makes up for the deficiency of the mask signal method and applies the improved EMD algorithm to the non-stationary signal processing in practical engineering. Continuous shearer is a large underground mining equipment, the main frequency of vibration signal is low frequency, taking the non-stationary signal of cutting arm of continuous mining machine as an example to study. First of all, the vibration signal of the cutting arm of the continuous mining machine is obtained by calculating the magnitude of the different mode motion energy at different points on the cutting arm of the continuous mining machine, and the installation position of the sensor is optimized, and the noise in the original signal is obtained. The high frequency noise in the signal is removed by the EMD method, and then the noise reduction signal is analyzed by the improved mask signal method, and the mode aliasing phenomenon in the EMD is successfully eliminated. It is shown that the energy mask signal method can also be used in practical engineering to eliminate the phenomenon of mode aliasing.
【学位授予单位】：中北大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TN911.7
本文编号：2282068
[Abstract]:In practical engineering applications, the obtained signals are generally non-stationary signals, which is very important for the analysis and processing of non-stationary signals. In the processing of these data sequences, the traditional time-frequency analysis methods often used in the past are based on Fourier transform, so there are some limitations in the processing of non-stationary signals. Empirical mode decomposition (EMD) is a new adaptive time-frequency analysis method proposed by Norden E.Huang et al., a Chinese scientist from NASA, in 1998 when analyzing nonstationary and nonlinear signals. Great improvement has been made in the traditional signal processing method, which is a modern signal processing method, and it does not require any prior knowledge. According to the characteristics of the signal itself, any complex non-stationary signal is decomposed into the sum of several intrinsic modal components (IMF) and a residue. After Fourier transform, all intrinsic modal components can obtain the physical instantaneous frequency of the original signal. Compared with the traditional signal processing method, the EMD method has more advantages and is widely used in graphic processing and signal processing. Many fields such as vibration test and mechanical fault diagnosis have achieved good results. In this paper, based on the study of the EMD algorithm, the existing mode aliasing is improved, and an energy-based mask signal method is proposed. According to the conservation law of energy, when there is no false mode component in intrinsic mode component, the energy conservation of decomposition process, the sum of energy of all components is equal to the energy of the original signal, but when there is false mode component, the energy is not conserved. The energy of the original signal is lower than the sum of the energy of each component, and the energy of the sum of any two components is also smaller than the sum of its energy. The main direction of the energy leakage is determined, and the influence of the energy leakage on the calculation of the frequency of the mask signal is reduced. It makes up for the deficiency of the mask signal method and applies the improved EMD algorithm to the non-stationary signal processing in practical engineering. Continuous shearer is a large underground mining equipment, the main frequency of vibration signal is low frequency, taking the non-stationary signal of cutting arm of continuous mining machine as an example to study. First of all, the vibration signal of the cutting arm of the continuous mining machine is obtained by calculating the magnitude of the different mode motion energy at different points on the cutting arm of the continuous mining machine, and the installation position of the sensor is optimized, and the noise in the original signal is obtained. The high frequency noise in the signal is removed by the EMD method, and then the noise reduction signal is analyzed by the improved mask signal method, and the mode aliasing phenomenon in the EMD is successfully eliminated. It is shown that the energy mask signal method can also be used in practical engineering to eliminate the phenomenon of mode aliasing.
【学位授予单位】：中北大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TN911.7
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