数学形态学在机械故障诊断中的应用研究
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摘要
机械故障诊断的关键是如何从故障信号中提取故障特征,信号分析是故障特征提取最常用的方法。机械系统发生故障时,振动信号往往具有非线性非平稳特征。因此如何结合故障信号特点并选择合适的方法,对非线性非平稳故障信号进行分析以提取故障特征是机械故障诊断领域需要重点研究的课题。传统非线性非平稳信号分析方法如短时傅里叶变换、小波变换、希尔伯特黄变换等都各自存在一定的局限性,因此迫切需要新的理论与信号分析方法以提高机械故障诊断的效率与技术水平。数学形态学是近年来发展起来的一种非线性信号分析方法,已经逐渐开始应用到机械故障诊断中并取得了较好的效果。本文以转子系统、齿轮和滚动轴承为研究对象,对基于数学形态学的机械故障诊断方法进行了深入的研究,主要工作和研究成果如下：
(1)介绍了转子系统、齿轮和滚动轴承这三种常见机械零件的振动故障机理,并对故障信号特征进行了分析,为后续章节的分析奠定基础。
(2)针对传统形态滤波结构元素选取随机的问题,首先提出一种自适应多尺度复合形态滤波降噪方法,该方法能够根据信号局部特征和噪声特点自适应的选择结构元素类型和尺寸大小,然后采用该方法对转子振动信号进行滤波,并通过关联维数对转子故障进行分类,最后通过仿真与实验数据验证了方法的有效性。
(3)针对传统形态学边缘检测边缘模糊的问题,首先提出一种基于多结构元素多尺度的形态学谱图边缘检测方法,该方法采用四个不同方向的多尺度结构元素提取齿轮故障时频图边缘特征,并通过与传统边缘检测效果比较以验证其有效性。然后计算边缘特征的灰度共生矩阵以提取特征量,最后通过LSSVM方法对齿轮故障进行识别。实例证明,该方法可有效的对故障进行分类。
(4)针对传统提升形态小波提升算子固定不变的局限,首先提出了一种自适应提升形态小波降噪的方法,该方法能够根据信号局部特征自适应选择提升算子,然后采用该方法提取滚动轴承故障特征,并在定义故障特征能量特征向量的基础上,采用灰色关联度方法对故障进行分类。实例表明该方法能够准确区分发生在不同部位的故障,但却难以对不同严重程度的故障进行有效分类。
(5)针对传统形态小波在分解过程中由于抽样而造成的信号逐层递减问题,首先在形态非抽样小波框架的基础上提出一种基于差值形态滤波的形态非抽样小波构造方法,并在定义形态非抽样小波能量特征向量与能量熵的基础上,提出了基于D-S证据理论的形态非抽样小波和基于过程的形态非抽样小波能量熵信息融合故障诊断方法。实测滚动轴承故障数据表明,以上两种方法不仅能够识别发生在不同部位的故障,还能够准确区分不同严重程度的故障。
The key issue of machinery fault diagnosis is how to extract fault feature from fault vibration signals, signal analysis methods are most widely used in fault feature extraction. The vibration signals are usually possess non-stationary and nonlinear signals when mechanical fault occurs. Therefore, how to extract fault feature from non-stationary and nonlinear signals is one of the important research issue in the field of mechanical fault diagnosis. However, the traditional time-frequency analysis method such as Short Fourier Transform(SFT), wavelet transform and Hilbert-Huang Transform(HHT) have their own limitations. Therefore, it is necessary to apply novelty signal processing theory and method to improve efficiency and level of fault diagnosis. Mathematical morphology, as a nonlinear analysis method, is recently developed and has began to be used in machinery fault diagnosis. It has also been proven to be effective in some areas of fault diagnosis. Setting rotor system, gear and rolling element bearing as research objects, this thesis studied deeply on fault diagnosis methods based on mathematical morphology. The main contents are as follows:
(1) Fault principle of common mechanical parts, such as rotor system, gear and rolling element bearing were introduced, fault signal feature analysis were also carried out to supply basis for latter chapters.
(2) In order to overcome the problem of random selection in traditional morphological filter, an adaptive multi-scale compound morphological filter (AMCMF) method was proposed. Types and size of structure element could be determined adaptively according to signal local characteristic and noise. Correlation dimension was applied to classify rotor system fault types after the signal was filtered by AMCMF. Test results indicate effectiveness of the method.
(3) Aiming at the problem of fuzzy edge in traditional morphological detection, a multi-structure multi-scale morphological edge detection method was proposed. Four direction structure element were used to extract edge in time-frequency chart. Its effectiveness could be demonstrated by comparing with traditional morphological edge detection. Feature could be extracted by calculating gray co-occurrence matrix. Gear fault could be classified through LSSVM.
(4) In order to overcome limitation of constant lifting operator in traditional lifting morphological wavelet, an adaptive lifting morphological wavelet(ALMW) denoising method was proposed. Lifting operator could be determined by signal local characteristic. Fault energy feature vector was defined after fault feature was extracted by ALMW. Gray relation was used to diagnosis gear fault. The test result indicated that faults on different parts could be classified while different severity fault could not.
(5) To avoid decreasing of the signal length in morphological wavelet decomposition, a new multiscale morphological undecimated wavelet decmopositon(MUWD) based on differential morphological filter was proposed. Morphological undecimated wavelet energy feature vector and energy entropy were defined. Based on morphological undecimated wavelet and process, information fusion fault diagnosis methods were proposed. The experiment indicated that both faults on different part and different severity faults could be distinguished.
(1)介绍了转子系统、齿轮和滚动轴承这三种常见机械零件的振动故障机理,并对故障信号特征进行了分析,为后续章节的分析奠定基础。
(2)针对传统形态滤波结构元素选取随机的问题,首先提出一种自适应多尺度复合形态滤波降噪方法,该方法能够根据信号局部特征和噪声特点自适应的选择结构元素类型和尺寸大小,然后采用该方法对转子振动信号进行滤波,并通过关联维数对转子故障进行分类,最后通过仿真与实验数据验证了方法的有效性。
(3)针对传统形态学边缘检测边缘模糊的问题,首先提出一种基于多结构元素多尺度的形态学谱图边缘检测方法,该方法采用四个不同方向的多尺度结构元素提取齿轮故障时频图边缘特征,并通过与传统边缘检测效果比较以验证其有效性。然后计算边缘特征的灰度共生矩阵以提取特征量,最后通过LSSVM方法对齿轮故障进行识别。实例证明,该方法可有效的对故障进行分类。
(4)针对传统提升形态小波提升算子固定不变的局限,首先提出了一种自适应提升形态小波降噪的方法,该方法能够根据信号局部特征自适应选择提升算子,然后采用该方法提取滚动轴承故障特征,并在定义故障特征能量特征向量的基础上,采用灰色关联度方法对故障进行分类。实例表明该方法能够准确区分发生在不同部位的故障,但却难以对不同严重程度的故障进行有效分类。
(5)针对传统形态小波在分解过程中由于抽样而造成的信号逐层递减问题,首先在形态非抽样小波框架的基础上提出一种基于差值形态滤波的形态非抽样小波构造方法,并在定义形态非抽样小波能量特征向量与能量熵的基础上,提出了基于D-S证据理论的形态非抽样小波和基于过程的形态非抽样小波能量熵信息融合故障诊断方法。实测滚动轴承故障数据表明,以上两种方法不仅能够识别发生在不同部位的故障,还能够准确区分不同严重程度的故障。
The key issue of machinery fault diagnosis is how to extract fault feature from fault vibration signals, signal analysis methods are most widely used in fault feature extraction. The vibration signals are usually possess non-stationary and nonlinear signals when mechanical fault occurs. Therefore, how to extract fault feature from non-stationary and nonlinear signals is one of the important research issue in the field of mechanical fault diagnosis. However, the traditional time-frequency analysis method such as Short Fourier Transform(SFT), wavelet transform and Hilbert-Huang Transform(HHT) have their own limitations. Therefore, it is necessary to apply novelty signal processing theory and method to improve efficiency and level of fault diagnosis. Mathematical morphology, as a nonlinear analysis method, is recently developed and has began to be used in machinery fault diagnosis. It has also been proven to be effective in some areas of fault diagnosis. Setting rotor system, gear and rolling element bearing as research objects, this thesis studied deeply on fault diagnosis methods based on mathematical morphology. The main contents are as follows:
(1) Fault principle of common mechanical parts, such as rotor system, gear and rolling element bearing were introduced, fault signal feature analysis were also carried out to supply basis for latter chapters.
(2) In order to overcome the problem of random selection in traditional morphological filter, an adaptive multi-scale compound morphological filter (AMCMF) method was proposed. Types and size of structure element could be determined adaptively according to signal local characteristic and noise. Correlation dimension was applied to classify rotor system fault types after the signal was filtered by AMCMF. Test results indicate effectiveness of the method.
(3) Aiming at the problem of fuzzy edge in traditional morphological detection, a multi-structure multi-scale morphological edge detection method was proposed. Four direction structure element were used to extract edge in time-frequency chart. Its effectiveness could be demonstrated by comparing with traditional morphological edge detection. Feature could be extracted by calculating gray co-occurrence matrix. Gear fault could be classified through LSSVM.
(4) In order to overcome limitation of constant lifting operator in traditional lifting morphological wavelet, an adaptive lifting morphological wavelet(ALMW) denoising method was proposed. Lifting operator could be determined by signal local characteristic. Fault energy feature vector was defined after fault feature was extracted by ALMW. Gray relation was used to diagnosis gear fault. The test result indicated that faults on different parts could be classified while different severity fault could not.
(5) To avoid decreasing of the signal length in morphological wavelet decomposition, a new multiscale morphological undecimated wavelet decmopositon(MUWD) based on differential morphological filter was proposed. Morphological undecimated wavelet energy feature vector and energy entropy were defined. Based on morphological undecimated wavelet and process, information fusion fault diagnosis methods were proposed. The experiment indicated that both faults on different part and different severity faults could be distinguished.
引文
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