旋转机械故障诊断的时频分析方法及其应用研究

发布时间：2018-05-28 13:53

本文选题：形态滤波 + 频率切片小波变换　；参考：《武汉科技大学》2014年博士论文

【摘要】：研究旋转机械的故障诊断技术，对于保障设备安全运行、减少重大的经济损失和避免灾难性事故的发生具有十分重要的意义。大多数旋转机械的振动信号是非平稳信号，时频分析方法能同时提取振动信号时域和频域的局部信息，适用于旋转机械故障诊断。但是短时傅立叶变换(Short Time Fourier Transform，STFT)、Winger-Ville分布、小波变换和Hilbert-Huang变换等时频分析方法都存在各自的缺陷，故迫切需要研究新的旋转机械故障诊断方法。本文对频率切片小波变换、局域均值分解、本征时间尺度分解方法的理论及其在旋转机械故障诊断中的应用进行了深入的研究。其主要内容如下: 1.信号中的噪声会降低频率切片小波变换分析的频率分辨率，为此，提出了基于形态滤波、自相关分析和频率切片小波变换的轴承故障诊断方法。提出一种多结构元素差值形态滤波器，它比单一结构元素的差值形态滤波器降噪效果好，仿真信号与轴承故障诊断实例的分析验证了该方法的有效性。提出了基于时延自相关和频率切片小波变换的齿轮故障诊断方法，对齿轮故障信号进行频率切片小波变换分析前，进行自相关降噪处理能突出故障特征，提高频率分辨率。 2.论述了LMD和1.5维谱原理，针对信号中混入的噪声对局域均值分解结果造成影响的问题，提出了一种局域均值分解和1.5维谱相结合的故障诊断方法。针对局域均值分解方法计算效率低的问题，提出了一种基于B样条插值的局域均值分解(B-spline LocalMean Decomposition，BLMD)方法，在此基础上，提出了基于BLMD的时频分析方法并应用到轴承和齿轮的故障诊断中。提出了基于BLMD与倒双谱的故障诊断方法并应用到轴承与齿轮故障诊断中，仿真信号的分析与轴承和齿轮故障诊断实例验证了该方法的有效性。 3.针对常用的非平稳信号处理方法的局限性以及本征时间尺度分解的失真问题，提出了B样条改进的本征时间尺度分解(BITD)方法，在此基础上，提出了基于BITD的局部能量谱方法。针对齿轮故障振动信号的非平稳特征，提出了B样条插值的本征时间尺度分解和同态滤波解调相结合的故障诊断方法。首先采用BITD方法对齿轮振动信号进行分解，将其分解为若干个合理旋转(Proper Rotation，PR)分量之和，然后用相关系数筛选出最能表征故障信息的PR分量进行同态滤波解调提取故障特征。仿真信号与齿轮故障诊断工程实例的分析验证了该方法的有效性。提出了基于BITD、能量算子和对角切片谱的旋转机械故障诊断方法，通过对仿真和实验信号的分析验证了该方法的有效性。 4.论述了随机共振降噪的原理，并结合BITD方法，提出将随机共振与BITD相结合的特征提取方法，并通过仿真信号与实验信号的分析验证了方法的有效性；研究基于EMD的信号降噪方法，在分析已有基于EMD降噪方法不足的基础上，提出两种基于BITD的阈值消噪方法，，并将其用于滚动轴承故障信号的降噪和特征提取技术中。通过仿真信号与实验信号的分析验证了该方法的有效性。 5.在论述排列熵(Permutation Entropy，PE)和基本尺度熵(Base-scale Entropy，BE)原理的基础上，提出了基于BITD和排列熵的滚动轴承障诊断方法，采用BITD方法对滚动轴承振动信号进行分解，再对得到的前4个合理旋转分量计算其排列熵，并将熵值作为特征向量输入支持向量机分类器，从而实现滚动轴承故障类别的诊断，实验数据分析结果表明，该方法能有效地实现滚动轴承故障类型的诊断。针对齿轮振动信号的非线性、非平稳特征和难以获取大量故障样本的问题，提出了BITD和基本尺度熵的齿轮故障诊断方法。首先采用BITD方法对齿轮振动信号进行分解，再对得到的第一个有意义的合理旋转分量计算其基本尺度熵，并将熵值作为特征向量输入支持向量机分类器，从而实现齿轮故障类别的诊断，实验数据分析的结果表明，该方法能有效地实现齿轮故障类型的诊断。
[Abstract]:The study of the fault diagnosis technology of rotating machinery is of great significance for ensuring the safe operation of the equipment, reducing the major economic losses and avoiding the occurrence of catastrophic accidents. Rotating machinery fault diagnosis. But short time Fu Liye transform (Short Time Fourier Transform, STFT), Winger-Ville distribution, wavelet transform and Hilbert-Huang transform have their own defects. Therefore, it is urgent to study the new method of rotating machinery fault diagnosis. In this paper, the frequency slice wavelet transform, local mean mean decomposition, The theory of intrinsic time scale decomposition and its application in fault diagnosis of rotating machinery are studied in detail.
The noise in the 1. signal will reduce the frequency resolution of the frequency slice wavelet transform analysis. Therefore, a bearing fault diagnosis method based on morphological filtering, autocorrelation analysis and frequency slice wavelet transform is proposed. A multi structure element difference morphological filter is proposed, which is better than the difference form filter of a single structure element. The validity of this method is verified by the analysis of real signal and bearing fault diagnosis example. A gear fault diagnosis method based on time delay autocorrelation and frequency slice wavelet transform is proposed. Before the frequency slice wavelet transform analysis of the gear fault signal, the autocorrelation noise reduction processing can highlight the fault features and improve the frequency resolution.
2. the principle of LMD and 1.5 dimensional spectrum is discussed. In view of the problem that the noise mixed in the signal affects the local mean decomposition results, a fault diagnosis method combining local mean mean decomposition and 1.5 dimensional spectrum is proposed. A local mean decomposition (B) spline interpolation based on local mean decomposition (local mean mean decomposition) is proposed. B-spline LocalMean Decomposition, BLMD) method, and on this basis, a time-frequency analysis method based on BLMD is proposed and applied to the fault diagnosis of bearing and gear. A fault diagnosis method based on BLMD and inverted bispectrum is proposed and applied to the fault diagnosis of bearing and gear, the analysis of the imitation true signal and the fault diagnosis of bearing and gear. The validity of the method is verified.
3. in view of the limitation of the commonly used nonstationary signal processing methods and the distortion of eigentime scale decomposition, the improved eigentime scale decomposition (BITD) method of B spline is proposed. On this basis, a local energy spectrum method based on BITD is proposed. The B spline interpolation is proposed for the non stationary characteristics of the vibration signal of the gear fault. The fault diagnosis method combined with eigentime scale decomposition and homomorphic filtering demodulation. First, the BITD method is used to decompose the gear vibration signal and decompose it into several reasonable rotation (Proper Rotation, PR) components, and then the PR component which can characterize the fault information is selected by the correlation coefficient to extract the homomorphic filter demodulation. The effectiveness of the method is verified by the analysis of simulation signals and gear fault diagnosis engineering examples. A fault diagnosis method for rotating machinery based on BITD, energy operator and diagonal slice spectrum is proposed. The effectiveness of the method is verified by the analysis of simulation and experimental signals.
4. the principle of random resonance noise reduction is discussed, and combined with BITD method, a feature extraction method combining random resonance with BITD is proposed, and the effectiveness of the method is verified by the analysis of the simulation signal and the experimental signal. The method of signal noise reduction based on EMD is studied and two kinds of methods are proposed on the basis of the analysis of the shortcomings of the existing EMD denoising methods. The method of threshold de-noising based on BITD is used in the technology of noise reduction and feature extraction of rolling bearing fault signals. The effectiveness of the method is verified by the analysis of simulation and experimental signals.
5. on the basis of the principle of Permutation Entropy (PE) and basic scale entropy (Base-scale Entropy, BE), the diagnosis method of rolling bearing barrier based on BITD and permutation entropy is proposed. The vibration signal of rolling bearing is decomposed by BITD method, and then the entropy is calculated by the first 4 reasonable rotating components and entropy value is used as a method. The feature vector input support vector machine classifier is used to diagnose the rolling bearing fault classification. The results of experimental data analysis show that the method can effectively diagnose the fault types of rolling bearings. In view of the nonlinear, non stationary characteristics of the gear vibration signal and the difficulty of obtaining a large number of fault samples, the BITD and the basic method are proposed. The gear fault diagnosis method of scale entropy. First, the BITD method is used to decompose the gear vibration signal, and then the first meaningful and reasonable rotation component is used to calculate its basic scale entropy, and the entropy value is used as the eigenvector to input the support vector machine classifier, thus the diagnosis of the gear fault classification is realized and the experimental data analysis is made. The results show that the method can effectively diagnose gear fault types.
【学位授予单位】：武汉科技大学
【学位级别】：博士
【学位授予年份】：2014
【分类号】：TH165.3

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