齿轮箱故障振动信号去噪及特征提取算法研究
发布时间：2018-07-17 00:23
【摘要】：齿轮箱作为机械设备中一种必不可少的连接和传递动力的通用零部件,在金属切削机床、航空、电力系统、农业机械、运输机械、冶金机械等现代工业设备中得到了广泛的应用。作为传动机械,其运行状态好坏与否,直接影响到整个机械传动组的工作性能。因此研究齿轮箱故障诊断技术和方法,对齿轮箱进行状态检测及故障分析诊断、确保齿轮箱正常工作具有重要意义。 齿轮箱故障诊断技术是通过分析处理齿轮箱运行时的状态信息。定量识别其技术状态,并诊断异常故障状态的一门多学科交叉的综合技术。齿轮箱振动信号中包含了大量的工作状态信息,利用齿轮箱振动信号诊断故障是一种行之有效的方法。应用振动信号分析法对齿轮箱进行故障诊断的过程中,滤波去噪处理、故障特征提取是其中尤为重要的两个问题,一直被公认为是故障诊断中的关键环节。本文从工程实际应用的角度出发,分别以风力发电机行星齿轮箱和通用工业齿轮箱故障振动信号为具体研究对象,综合应用谱峭度、黄金分割、下山单纯形、小波分析、快速傅里叶变换、粒子群优化、冲击响应谱、瞬态分析等理论,基于混合优化理念,对齿轮箱故障振动信号去噪及故障特征提取算法两个关键问题进行了系统地研究,为齿轮箱故障诊断技术的开发研究提供了一定的理论支持。 本文重在研究齿轮箱故障振动信号的优化滤波去噪和故障脉冲瞬态特征提取算法。 (1)分析了齿轮箱振动信号常用分析方法的基本原理及适用范围,为后续的振动信号去噪、故障特征提取算法研究提供一定的理论支持。 (2)为了解决单一的黄金分割法优化速度较慢的问题,将黄金分割和抛物线插值两种算法相结合构成加速一维搜索算法。 (3)为了解决单一的传统多维优化算法收敛速度较慢,且收敛时容易陷入局部极值的问题。以峭度最大值为目标函数,根据振动信号频域分析和小波分析的原理,基于一维搜索-多维搜索(参数粗调-参数微调)混合优化的自适应滤波算法,分别采用了两种不同的滤波手段。切比雪夫带通滤波和Morlet小波滤波。应用混合优化算法,即谱峭度-加速一维搜索算法-下山单纯形法分别优化切比雪夫带通滤波器和Morlet小波滤波器的设计参数,对齿轮箱故障振动信号进行滤波去噪仿真处理。 (4)为了对比不同类型混合优化算法的优劣性,将以下四种优化算法：谱峭度、黄金分割、下山单纯形、遗传算法,按一维搜索-多维搜索模式混合优化切比雪夫带通滤波器参数,并进行去噪仿真实验。 (5)在前期研究的基础上将粒子群优化算法应用到齿轮箱故障振动信号去噪中,将基于加速一维搜索-粒子群优化的混合优化算法用于优化切比雪夫带通滤波器和Morlet小波滤波器的相关参数,并对齿轮箱故障振动信号进行滤波去噪仿真处理。 (6)为了提取能够反映齿轮箱工作信息变化情况以及故障未来发展趋势的故障脉冲瞬态特征,将冲击响应谱分析和瞬态分析法应用到齿轮箱齿轮的故障特征提取中,用于提取衡量齿轮箱故障严重程度的三个瞬态特征指标：冲击响应谱指标SRS以及齿轮啮合系统的固有频率ωn、振荡阻尼比ζ。
[Abstract]:As a necessary part of the mechanical equipment to connect and transmit power, the gear box is widely used in the modern industrial equipment such as metal cutting machine, aviation, power system, agricultural machinery, transportation machinery and metallurgical machinery. As a transmission machine, its running state is directly affected by the whole machine transmission. Therefore, it is important to study the fault diagnosis technology and method of the gear box, to detect the gear box and to diagnose the fault, so as to ensure the normal work of the gear box.
The gear box fault diagnosis technology is a multidisciplinary and interdisciplinary technology which can identify the state of the gear box and diagnose the state of the gear box, and diagnose the abnormal state of the fault. The vibration signal of the gear box contains a lot of work state information. It is effective to diagnose the fault by using the vibration signal of the gear box. In the process of fault diagnosis of gear box by vibration signal analysis, filtering de-noising and fault feature extraction are two important problems, which have always been recognized as the key link in fault diagnosis. This paper, from the angle of practical application of the engineering, uses the planetary gearbox of wind turbine and general purpose respectively. The fault vibration signal of industrial gear box is a specific research object, and two key questions, such as spectral kurtosis, gold segmentation, downhill simplex, wavelet analysis, fast Fourier transform, particle swarm optimization, impact response spectrum, transient analysis, etc., are used to denoise and extract fault characteristics of gearbox vibration signal based on mixed optimization idea. A systematic study is carried out to provide some theoretical support for the development and research of gearbox fault diagnosis technology.
This paper focuses on the research of gearbox fault vibration signal optimization filter denoising and fault pulse transient feature extraction algorithm.
(1) the basic principle and application range of the common analysis method of the vibration signal of the gear box are analyzed. It provides a certain theoretical support for the subsequent vibration signal denoising and the research of the fault feature extraction algorithm.
(2) in order to solve the problem of slow optimization of the single gold segmentation method, the gold segmentation and parabolic interpolation are combined to form the accelerated one dimension search algorithm.
(3) in order to solve the problem that the convergence speed of the single traditional multi-dimensional optimization algorithm is slow and the convergence is easy to fall into the local extremum, the adaptive filtering algorithm based on one dimension search multidimensional search (parameter coarse tuning and parameter tuning) is based on the principle of the frequency domain analysis and the wavelet analysis of the vibration signal, with the maximum kurtosis as the objective function. Two different filtering methods are adopted. Chebyshev bandpass filter and Morlet wavelet filter are used respectively. The design parameters of the Chebyshev bandpass filter and Morlet wavelet filter are optimized by using the hybrid optimization algorithm, namely the spectral kurtosis acceleration one dimension search algorithm - the downhill simplex method, respectively, to filter the noise of the gear box fault vibration signal. Simulation processing.
(4) in order to compare the advantages and disadvantages of different types of hybrid optimization algorithms, the following four optimization algorithms: spectral kurtosis, gold segmentation, downhill simplex, genetic algorithm, and one dimensional search - multidimensional search model are used to optimize the Chebyshev bandpass filter parameters and carry out noise removal experiments.
(5) the particle swarm optimization algorithm is applied to the de-noising of the gear box fault vibration signal on the basis of the previous research, and the hybrid optimization algorithm based on the acceleration one dimension search particle swarm optimization is used to optimize the related parameters of the Chebyshev bandpass filter and Morlet wavelet filter, and the vibration signal of the gear box fault vibration is filtered to imitate the noise. Deal with it.
(6) in order to extract the transient characteristics of the fault pulse which can reflect the change of the working information of the gear box and the future development trend of the fault, the impact response spectrum analysis and the transient analysis method are applied to the fault feature extraction of the gear box gear, which is used to extract the three transient characteristic indexes of the severity of the gear box fault: the impact response spectrum The index SRS and the natural frequency of gear meshing system are n, damping ratio.
【学位授予单位】：东北林业大学
【学位级别】：博士
【学位授予年份】：2014
【分类号】：TH132.41;TH165.3
本文编号：2128205
[Abstract]:As a necessary part of the mechanical equipment to connect and transmit power, the gear box is widely used in the modern industrial equipment such as metal cutting machine, aviation, power system, agricultural machinery, transportation machinery and metallurgical machinery. As a transmission machine, its running state is directly affected by the whole machine transmission. Therefore, it is important to study the fault diagnosis technology and method of the gear box, to detect the gear box and to diagnose the fault, so as to ensure the normal work of the gear box.
The gear box fault diagnosis technology is a multidisciplinary and interdisciplinary technology which can identify the state of the gear box and diagnose the state of the gear box, and diagnose the abnormal state of the fault. The vibration signal of the gear box contains a lot of work state information. It is effective to diagnose the fault by using the vibration signal of the gear box. In the process of fault diagnosis of gear box by vibration signal analysis, filtering de-noising and fault feature extraction are two important problems, which have always been recognized as the key link in fault diagnosis. This paper, from the angle of practical application of the engineering, uses the planetary gearbox of wind turbine and general purpose respectively. The fault vibration signal of industrial gear box is a specific research object, and two key questions, such as spectral kurtosis, gold segmentation, downhill simplex, wavelet analysis, fast Fourier transform, particle swarm optimization, impact response spectrum, transient analysis, etc., are used to denoise and extract fault characteristics of gearbox vibration signal based on mixed optimization idea. A systematic study is carried out to provide some theoretical support for the development and research of gearbox fault diagnosis technology.
This paper focuses on the research of gearbox fault vibration signal optimization filter denoising and fault pulse transient feature extraction algorithm.
(1) the basic principle and application range of the common analysis method of the vibration signal of the gear box are analyzed. It provides a certain theoretical support for the subsequent vibration signal denoising and the research of the fault feature extraction algorithm.
(2) in order to solve the problem of slow optimization of the single gold segmentation method, the gold segmentation and parabolic interpolation are combined to form the accelerated one dimension search algorithm.
(3) in order to solve the problem that the convergence speed of the single traditional multi-dimensional optimization algorithm is slow and the convergence is easy to fall into the local extremum, the adaptive filtering algorithm based on one dimension search multidimensional search (parameter coarse tuning and parameter tuning) is based on the principle of the frequency domain analysis and the wavelet analysis of the vibration signal, with the maximum kurtosis as the objective function. Two different filtering methods are adopted. Chebyshev bandpass filter and Morlet wavelet filter are used respectively. The design parameters of the Chebyshev bandpass filter and Morlet wavelet filter are optimized by using the hybrid optimization algorithm, namely the spectral kurtosis acceleration one dimension search algorithm - the downhill simplex method, respectively, to filter the noise of the gear box fault vibration signal. Simulation processing.
(4) in order to compare the advantages and disadvantages of different types of hybrid optimization algorithms, the following four optimization algorithms: spectral kurtosis, gold segmentation, downhill simplex, genetic algorithm, and one dimensional search - multidimensional search model are used to optimize the Chebyshev bandpass filter parameters and carry out noise removal experiments.
(5) the particle swarm optimization algorithm is applied to the de-noising of the gear box fault vibration signal on the basis of the previous research, and the hybrid optimization algorithm based on the acceleration one dimension search particle swarm optimization is used to optimize the related parameters of the Chebyshev bandpass filter and Morlet wavelet filter, and the vibration signal of the gear box fault vibration is filtered to imitate the noise. Deal with it.
(6) in order to extract the transient characteristics of the fault pulse which can reflect the change of the working information of the gear box and the future development trend of the fault, the impact response spectrum analysis and the transient analysis method are applied to the fault feature extraction of the gear box gear, which is used to extract the three transient characteristic indexes of the severity of the gear box fault: the impact response spectrum The index SRS and the natural frequency of gear meshing system are n, damping ratio.
【学位授予单位】：东北林业大学
【学位级别】：博士
【学位授予年份】：2014
【分类号】：TH132.41;TH165.3
【参考文献】
相关期刊论文 前10条
1 石喜玲;孙运强;鲁旭涛;;小波分析在扭矩动态测试法去噪中的应用[J];弹箭与制导学报;2004年S9期
2 ;Application of Wavelets Transform to Fault Detection in Rotorcraft UAV Sensor Failure[J];Journal of Bionic Engineering;2007年04期
3 王毅,牛奕龙,陈海洋;独立分量分析的基本问题与研究进展[J];计算机工程与应用;2005年27期
4 丁康,江利旗;解调分析在机械振动分析中应用的局限性研究[J];机械科学与技术;2000年05期
5 陈雪峰;李继猛;程航;李兵;何正嘉;;风力发电机状态监测和故障诊断技术的研究与进展[J];机械工程学报;2011年09期
6 焦新涛,丁康;加窗频谱分析的恢复系数及其求法[J];汕头大学学报(自然科学版);2003年03期
7 刘刚,屈梁生;应用连续小波变换提取机械故障的特征[J];西安交通大学学报;2000年11期
8 王晓冬;何正嘉;訾艳阳;;滚动轴承故障诊断的多小波谱峭度方法[J];西安交通大学学报;2010年03期
9 ;CLASSIFICATION OF GEAR FAULTS USING HIGHER-ORDER STATISTICS AND SUPPORT VECTOR MACHINES[J];Chinese Journal of Mechanical Engineering(English Edition);2002年03期
10 ;NEW DEMODULATION TECHNOLOGY OF OPTIMIZING ENERGY OPERATOR AND APPLICATION TO GEAR FAULT DIAGNOSIS[J];Chinese Journal of Mechanical Engineering;2005年04期
相关博士学位论文 前1条
1 孔德文;大型齿轮传动装置动力学及故障诊断技术研究[D];吉林大学;2008年
，本文编号：2128205
本文链接：https://www.wllwen.com/kejilunwen/jixiegongcheng/2128205.html
