【作者】 程发斌；

【导师】 汤宝平；

【作者基本信息】 重庆大学 ， 机械电子工程， 2007， 博士

【摘要】 在机械故障诊断的发展过程中,最重要、最关键、也是最困难的问题之一就是故障特征信号的特征提取。动态信号的复杂性和特征提取的多学科交叉融合特性使得信号特征提取方法一直是人们广为关注的重要研究方向,其中信号的消噪预处理,时频分布的交叉项抑制,瞬时频率特征提取是研究的主要内容。本文综合利用非平稳、非高斯信号处理中最受关注的Wigner-Ville分布(WVD)、小波变换(WT)和Hilbert-Huang变换(HHT)等时频处理方法的优点,提出了面向机械故障特征提取的混合时频分析(Hybrid Time-Frequency Analysis, HTFA)方法,对机械振动信号的降噪、故障特征提取技术进行了深入的研究。主要研究工作如下:在信号去噪方面,提出了基于最优Morlet小波和奇异值分解(SVD)的滤波消噪方法。机械振动信号的消噪对有效揭示机器故障信息具有重要意义。本文分析了传统的小波去噪方法及小波变换的滤波特性,利用小波变换技术、奇异值分解技术和Morlet小波良好的时域和频域特性,提出了基于最优Morlet小波和SVD的滤波消噪方法。该方法可以很好的降低噪声信号,有效的提取信号中周期成分,具有良好的瞬态信息提取能力,为正确识别故障特征提供了有力的保证。在基于时频分布的特征提取方面,提出一种基于自适应短时傅里叶变换(ASTFT谱)抑制WVD交叉项的特征提取方法。时频分析方法已广泛应用于故障特征提取,然而以Wigner-Ville分布为基础的时频分析方法最大的不足就是交叉项的干扰。分析了WVD交叉项产生的原因及自项与交叉项的相互关系,研究了抑制交叉项的核函数法和分量组合法。提出一种基于自适应短时傅里叶变换抑制WVD交叉项的特征提取方法,并将该方法用于轴承故障的特征提取,很好地抑制了交叉项的产生,同时也保留了WVD分布的优良特性,为故障诊断提供了一种有效的分析方法。在瞬时特征提取方面,提出一种基于小波包和改进HHT的瞬时特征提取方法。分析了小波包分析理论和HHT的原理,研究了经验模态分解(EMD)的完备性和近似正交性,提出一种基于小波包和改进HHT的瞬时特征提取方法。该方法可以很好的解决EMD带来的模态混叠现象,还可以减少噪声对信号的干扰,同时减少了EMD过程的计算量和分解层数,有利于故障特征的分析和提取。在完善HHT分析方法方面,提出了基于新的曲线拟合及边界处理算法的改进HHT分析方法。HHT是一种新的具有自适应的时频分析方法,但是HHT目前还只能算是一种经验方法,其理论依据尚不完备,有待于进一步的完善。分析了三次样条插值法进行包络线或均值线拟合时容易引起过冲和欠冲的根本原因,提出了基于B样条曲线的分段插值算法及混合插值算法,解决了三次样条插值算法容易引起的过冲和欠冲现象,同时也有效的解决了模态混叠现象。对于HHT的边界效应,分析了几种典型边界处理算法的本质、特点、优点和不足,提出了改进包络延拓法及边界极值加权法,有效地抑制了HHT的边界效应。从而提高了HHT在信号特征提取的合理性和准确性。在应用系统开发方面,成功研发了面向机械故障特征提取的非平稳信号分析系统。研究了共享参数模型的软件体系结构,设计了系统模块的统一构架和仪器界面,实现了基于混合时频分析方法的非平稳信号分析系统,并通过实际应用验证了系统的实用性和有效性。文章最后对本文工作进行了总结和对故障特征提取技术的研究进行了展望。更多还原

【Abstract】 The most important and crucial problem in the mechanical fault diagnosis is the feature extraction method of the fault characteristic signal, while it is the very problem most difficult to solve. Because of the complexity of the dynamic signal and the multidisciplinary cross and fusion characteristic of the extracted signal, the feature extraction method has been the most important research direction concerned by the researchers, in which signal de-noising, cross-terms suppressing of time-frequency distribution, instantaneous frequency feature extraction are the main elements. Make use of the non-stationary and non-gaussian signal processing methods merits such as Wavelet Transform (WT), Wigner-Ville Distribution (WVD) and Hilbert-Huang Transform (HHT), the hybrid time-frequency analysis methods for mechanical fault feature extraction are put forward, and this dissertation investigates noise reducing method of the machinery vibration signal and the feature extraction technique of the mechanical equipment thoroughly. Its contributions list as follows:(1) In terms of signal de-noising, a new method of filtering and de-noising based on optimal Morlet wavelet and Singular Value Decomposition (SVD) is put forward.Feature extraction and signal de-noising of the fault signal have been the most important investigation in the signal processing. By reducing the noise of machinery vibration signal, the mechanical fault information can be obtained effectively. This paper analyses the traditional wavelet de-noising method and the filter characteristic of wavelet transform, proposes a new method of filtering and de-noising based on the Morlet wavelet and SVD by using wavelet transform, singular value decomposition technology and the fine time-frequency characteristic of Morlet wavelet. The new de-noising method, which possesses better transient information extraction ability, could reduce the noise and extract the period of the signal effectively and assure the validity of the fault feature recognition.(2) In terms of characteristic improving based on time-frequency distribution, a new feature extraction method based on Adaptive Short-Time Fourier Transform (ASTFT), which could restrain the WVD cross-terms effectively, is put forward. Time-frequency analysis has widely been used for fault feature extraction, while Wigner-Ville distribution based time-frequency method has a supreme defect that there exists interfere of the cross-terms. After investigating the cause of WVD cross-terms, the correlation between auto-terms and cross-terms and the kernel function and component combination for cross-terms suppression, this paper proposes a new fault feature extraction method based on ASTFT to suppress the cross-terms of WVD effectively. Using on bearing fault feature extraction, the new method suppress the WVD cross-terms, reserve the fine characteristics of WVD, and provide an effective analyses method for fault diagnosis.(3) In terms of transient characteristic improving, a new transient characteristic extraction method based on Wavelet Packet transform (WPT) and Hilbert-Huang transform was put forward. After investigating the theory of WPT and HHT, the WPT based de-noising method and the integrity and approximate orthogonality of the EMD, this paper propose a new transient characteristic extraction method based on Wavelet Packet transform and Hilbert-Huang transform, which could eliminate the Modal Mixture of the EMD, decrease the noise interference and the computation of EMD process. The new method is propitious to the fault feature analysis and extraction.(4) In terms of HHT improving, some new algorithms for curve fitting and boundary processing are proposed. Although, as the new theory for adaptive time-frequency analysis, HHT is considered to be experiential method, it’s necessary to be consummated. When curve fitting for envelope or mean with cubic spline interpolation, its easily to come into contact with overshoot and undershoot problems. Aim at solve the disfigurement, the subsection interpolation arithmetic based on B spline curve and mixed interpolation curve arithmetic are proposed. Furthermore, it’s the effective technique to avoid the aliasing phenomena. In allusion to boundary effect of HHT, After investigating the hypostasis, peculiarity, advantage and disadvantage for some typical boundary process methods, this paper bring forward the improved extending envelope method and edge extrema-powered method, with the methods, it’s effectively restrain the boundary effect, and improves the rationality and veracity of the signal feature extraction with HHT.(5) In terms of system exploitation, successfully explore the non-stationary signal analysis system for mechanical fault feature extraction. Investigating the system structure of the parameter-sharing module software, designed the uniform framework of the system module and the apparatus interface, successfully implemented the non-stationary signal analysis system for mechanical fault feature extraction based hybrid time-frequency analysis methods, and proved to be practical and availability by some project applications. There are the summarization of the article and expectation of the feature extraction technology development in the end of article.更多还原