【作者】 蒋永华；

【导师】 汤宝平；

【作者基本信息】 重庆大学 ， 机械电子工程， 2010， 博士

【摘要】 基于振动信号处理的特征提取方法是旋转机械故障特征提取最常用方法。由于旋转机械在运行中受到多种因素的影响,其振动信号往往是非平稳信号,另外,现场采集的信号经常受到各种噪声干扰的影响,有用信号往往被淹没在强噪声背景中,因此,非平稳信号的微弱故障特征提取是当前的一个研究热点。本文基于小波变换、HHT、重分配尺度谱和魏格纳时频谱等时频分析方法,以及奇异值分解(SVD)、形态滤波等降噪技术,提出了三类旋转机械非平稳信号微弱故障特征提取方法,并将这些微弱特征提取方法与支持向量机(SVM)相结合应用于旋转机械的故障诊断中。本文的主要研究内容如下:在波形特征提取方面,研究了基于小波变换的微弱特征提取方法。利用小波变换的滤波特性和Morlet小波良好的时域和频域特性,提出了一种基于参数优化Morlet小波变换的微弱故障特征提取方法。利用最小Shannon熵方法优化Morlet小波的带宽参数,实现其与冲击特征成分的较优匹配,再根据小波系数矩阵的奇异值曲线中主要反映突变信息的过渡阶段求得最佳变换尺度。进一步的研究发现该方法依然存在着不足,为此对其进行了改进,提出了一种基于尺度周期性指数谱(SPE)的自适应Morlet小波微弱特征提取方法,利用修正的Shannon熵方法同时优化Morlet小波的中心频率与带宽参数,实现其与冲击特征成分的最优匹配,再根据得到的SPE谱求得最佳的小波变换尺度。试验信号分析和实际工程应用结果验证了该方法的有效性和优越性。在谱图特征提取方面,对基于重分配谱的微弱特征提取方法进行了系统的研究。针对重分配小波尺度谱存在着时、频分辨率不能同时达到最佳及当振动信号中存在着能量较大的噪声时会降低其时频分布可读性的缺陷,提出了一种重分配小波尺度谱的时频分布优化方法。首先优化重分配尺度谱基函数的时间-带宽积(TBP),克服其时、频分辨率不能同时达到最佳的缺陷,再对重分配尺度谱进行SVD降噪降低噪声干扰影响,提高其时频分布的可读性。针对魏格纳时频谱存在的问题,提出了一种基于重分配魏格纳时频谱和SVD的微弱特征提取方法。利用重分配算法对魏格纳时频谱进行重分配,提高魏格纳时频谱的时频聚集性,再对重分配时频谱进行SVD降噪,提高其谱图的可读性。此外,针对基于小波尺度谱的模极大值法提取小波脊线存在着受噪声干扰影响大、高频部分频率分辨率低等不足,提出了一种基于最优重分配小波尺度谱的小波脊线提取方法,并将其应用于齿轮箱的故障分析中。在瞬时特征提取方面,研究了基于形态奇异值分解和HHT的微弱特征提取方法。针对现场采集信号中的随机噪声和局部强干扰影响EMD分解质量的问题,提出一种形态奇异值分解滤波消噪方法,并将其与HHT相结合形成一种新的微弱故障特征提取方法。该方法首先对原始振动信号进行相空间重构和奇异值分解,根据奇异值分布曲线确定降噪阶次进行SVD降噪,再形态滤波,最后把消噪后的信号进行EMD分解,利用本征模模态分量(IMF)提取故障特征信息。对仿真信号和试验信号的应用分析结果表明,该方法能有效提取微弱故障特征,还可以减少EMD的分解层数和边界效应,提高EMD分解的时效性和精确度。在故障诊断方面,研究了基于微弱特征提取和支持向量机(SVM)的故障诊断方法。利用SVM出色的多类分类性能和小生境遗传算法优化参数的全局寻优能力,分别结合自适应Morlet小波变换和形态奇异值分解-EMD的微弱特征提取方法,实现了基于微弱特征提取和遗传优化SVM的故障诊断方法,滚动轴承故障诊断实例验证了其有效性和可行性。在软件开发方面,研发了基于本文所提方法的非平稳信号微弱特征提取模块。采用面向对象的编程技术以及VC++开发工具,设计开发了该模块,为旋转机械微弱特征提取和故障诊断提供了一个有效的分析工具,并通过实际应用验证了该模块的有效性和实用性。文章最后对本文的工作进行了总结,并展望了下一步的研究方向。更多还原

【Abstract】 Vibration signals analysis is one of the most important methods used for feature extraction and fault diagnosis. However, various kinds of factors, such as the change of the environment and the faults from the machine itself, often make the output signals of the running rotating machinery be non-stationary. Usually, these non-stationary signals contain abundant information about machine faults; therefore, it is important to analyse the non-stationary signals. Furthermore, these vibration signals sampled on the spot often contain amount of noise. If the background noise is too heavy, the useful information will be submerged, so the weak feature extraction of nonstationary signals is one of the current research focuses. With this background, based on time-frequency analysis methods of signal analysis including wavelet tansform, Hilbert-Huang transform (HHT), reassigned wavelet scalogram, reassigned Wigner-Ville distribution spectrogram, and denoising technology inchuding singular value decomposition (SVD), morphology filter, three types of weak feature extraction methods of non-stationary signals are proposed. In addition, condition identification is another key issue. Therefore, SVM is researched and introduced into rotating machinery fault diagnosis combined with these weak feature extraction methods. The main research work and conclusions are as follows:①In terms of waveform-based feature extraction, weak feature extraction method based on wavelet transform is researched in detail. A weak feature extraction method based on parameter optimized Morlet wavelet transform is proposed by using excellent filtering and time-frequency characteristics of Morlet wavelet. Minimum Shannon entropy is used to optimize the Morlet wavelet bandwidth parameter in order to achieve match with the impact component. Then, an abrupt information detection method based on the transitional stage of singular curve of wavelet coefficients’matrix is used to choose the appropriate scale for the wavelet transformation. Furthermore, the proposed method is improved upon and a weak feature extraction method with adaptive Morlet wavelet based on scale periodical exponential spectrum (SPE) is proposed. Modified Shannon entropy is used to optimize central frequency and bandwidth parameter of the Morlet wavelet in order to achieve optimal match with the impact component. Then, SPE spectrum obtained by carrying singular value decomposition (SVD) to the matrix is utilized to choose the appropriate scale for the wavelet transform. Simulation and application results show that the proposed method can be effecitively applied for weak feature extraction.②In terms of spectrum-based feature extraction, weak feature extraction method based on reassigned spectrum is discussed in detail. According to the fact that the resolutions of time-frequency of reassigned scalogram cannot simultaneously attain the best and the readability of time-frequency representation of it would also be reduced when there exist strong noise in a signal, a novel method to improve the readability of time-frequency representation of reassigned scalogram based on parameter optimization and SVD is proposed to overcome this shortcoming. The time-bandwidth product (TBP) of the wavelet basis is optimized by Shannon entropy, so the problem that the resolutions of time-frequency of reassigned scalogram cannot simultaneously attain the best is solved. Then, SVD de-noising is applied to the reassigned wavelet scalogram to reduce the influence of the noise. In order to overcome the shortcoming of reassigned Wigner-Ville distribution spectrogram (RWVDS), a weak feature extraction method based on RWVDS and SVD is proposed. The RWVDS is obtained by using the reassigned algorithm to the WVDS. Then, SVD de-noising is applied to the RWVDS to improve the readability of it. In addtion, in order to improve the precision of wavelet ridge extracted by modulus maximum method based on scalogram, a new method for extracting wavelet ridge based on optimal reassigned wavelet scalogram is proposed. The results of simulation and experiment show that the proposed method is feasible and effective for extracting weak feature of mechanical vibration signals with heavy background noise.③In terms of transient feature extraction, weak feature extraction method based on SVD-morphology filter and HHT is investigated in detail. Due to the influence caused by random noises and local strong disturbances embedded in signal on empirical mode decomposition (EMD) results, a novel integrated SVD-morphology filter method is proposed to overcome this shortcoming. And combining with EMD, a weak feature extraction method is proposed. Firstly, reconstruct the original vibration signal in phase space and decompose the attractor track matrix by singular value decomposition, and then select a reasonable order for noise reduction according to the singular curve. Secondly, filter the de-noised signal by morphology filter. Finally, decompose it by EMD to extract the intrinsic mode functions (IMF) for fault feature extraction. Experimental results show that this method could extract weak feature of rolling bearing effectively, reduce decomposition levels and boundary effect of EMD, and improve the timeliness and precision of EMD.④In terms of fault diagnosis, fault diagnosis method based on weak feature extraction and SVM is researched. Using the excellent multi-class classification performance of SVM and the global search capability of optimization parameters of niche genetic algorithm (NGA), combining the weak feature extraction method of adapative Morlet wavelet or SVD-morphology filter-EMD, a fault diagnosis method based on weak feature extraction and NGA-SVM is proposed. The experimental results of rolling bearing demonstrate the proposed diagosis approach is effective.⑤In terms of software development, a weak feature extraction module of nonstaionary signal of rotating machinery is developed successfully, including wavelet-based sub-module、HHT-based sub-module and spectrum-based sub-module. The module based on time-frequency analysis methods is implemented by using object-orientied programming technology and VC++ 6.0. Finally, the weak feature extraction module is applied to engineering applications and proved to be effective and pratical.At the end of the thesis, the summarization of the thesis and expectation of the feature extraction technology development are presented.更多还原