【作者】 苏文胜；

【导师】 马孝江；

【作者基本信息】 大连理工大学 ， 机械电子工程， 2010， 博士

【摘要】 滚动轴承是各种旋转机械中应用最广泛的一种通用部件,其运行状态往往直接影响整台机器的性能,对滚动轴承的状态监测和故障诊断具有重要的现实意义和经济价值。本文以滚动轴承为研究对象,从故障轴承振动信号的特征入手,针对滚动轴承诊断中的关键技术：解调、降噪和特征参数提取问题,应用现代信号处理技术,对滚动轴承的状态监测与故障诊断技术开展了一系列的研究工作。论文的主要工作内容如下：1.在分析滚动轴承振动机理的基础上,总结了滚动轴承局部损伤类故障振动信号具有周期性冲击和幅值调制的特征,指出滚动轴承故障诊断的关键问题是解调、降噪和特征参数提取。只要解决好这三个问题,就能实现滚动轴承故障的准确诊断。2.提出了基于EMD降噪和谱峭度法的滚动轴承故障诊断方法。总结了基于经验模式分解(EMD)的降噪方法有两种：基于阈值处理和基于滤波处理的EMD降噪方法。基于阈值处理方法中,借鉴了小波阈值降噪的思想；基于滤波处理方法中,针对故障轴承振动信号特点,提出了两个降噪准则。以仿真轴承故障信号为例,比较了两种方法的降噪性能,指出基于滤波处理的EMD降噪方法更适合作为滚动轴承信号预处理手段。针对共振解调中带通滤波器参数难以确定的问题,引入了谱峭度理论,根据谱峭度值最大处的频带确定共振频带。将EMD降噪和谱峭度理论相结合,提出了一种基于EMD降噪和谱峭度法的滚动轴承故障诊断方法,并以工程实际信号进行分析验证。3.提出了一种基于最优Morlet小波滤波器和自相关增强算法的滚动轴承故障自动诊断方法。为了消除干扰振动的频率成分,首先将实测轴承信号通过一个由Morlet小波确定的带通滤波器,该滤波器参数由遗传算法优化得到,讨论了遗传算法优化问题中的目标函数和约束条件。经过最优Morlet小波滤波后,信噪比得到显著提高。为了进一步减少残余的带内噪声,突出周期性冲击特征,提出了一种自相关增强算法,并将其用于Morlet小波滤波后的信号。在得到的自相关增强包络功率谱中,只有简单的几根谱线存在,有故障时对应故障特征频率,无故障时对应轴的转频,这对于操作者识别轴承故障类型非常容易,本方法可以几乎以自动的方式执行,仿真和试验结果验证了该方法非常适合滚动轴承的诊断。4.提出了一种基于双树复小波域隐Markov树模型的滚动轴承故障信号的降噪方法。针对传统离散小波变换具有平移敏感性和复小波变换不能完美重构的缺陷,提出采用双树复小波变换处理滚动轴承信号的方法。针对传统小波降噪方法没有考虑小波系数间相关性和非高斯性而造成降噪效果不够理想的问题,引入小波域隐Markov树模型的降噪思想。将双树复小波变换和小波域隐Markov树模型相结合,提出一种更有优势的降噪方法。根据双树复小波变换系数的实部和虚部是同时考虑还是分开考虑的原则,提出两种基于双树复小波域隐Markov树模型降噪方法,并比较它们与传统方法的性能,指出同时考虑实部和虚部的方法效果更好,并将该方法用于实际滚动轴承故障诊断进行验证。5.提出了一种基于小波包样本熵的滚动轴承故障诊断和预测方法。将信息论中的样本熵引入到机械故障诊断领域,讨论了样本熵的性能和计算参数的选择,指出无论在抗噪性、对样本长度的要求、还是反映系统的本质上,样本熵都比近似熵具有更好的性能。根据小波包分解子带能量越大故障信息越明显的特点,提出小波包样本熵的概念,较好地区分了轴承故障类型。接着将小波包样本熵用于滚动轴承故障趋势预测中,计算全寿命周期轴承试验台数据的小波包样本熵,利用EMD提取其中趋势,可以较好地预测滚动轴承的运行状态,比RMS值和峭度值更早地预报了故障的发展,说明小波包样本熵可以作为一种较好的轴承监测预报工具。6.开发了基于LabView和Matlab混合编程的滚动轴承振动信号分析系统。介绍了系统开发环境和总体结构设计,讨论了各个模块的实现方法,并通过实际信号进行功能演示,验证了本系统的方便快捷和有效性。更多还原

【Abstract】 Rolling element bearing is one of the most widely used parts in a varity of rotating machinery, whose running state often directly affects the performance of the whole machine, so the condition monitoring and fault diagnosis of rolling bearing has important practical significance and economic value. In this paper, rolling bearing is the research object. Starting with the characteristic of faulty bearing signal, aiming at the key technologies in bearing fault diagnosis, i.e., demodulation, denoising and feature extraction, and applying modern signal processing techniques, a series of studies on the condition monitoring and fault diagnosis of rolling bearing are carried out. The main works of this dissertation are listed as follows:1. Based on the analysis of rolling bearing vibration mechanism, the vibration signal characteristics of the bearing with local damage are summarized as cyclical shock and amplitude modulation. Then the key issues of rolling bearing fault diagnosis are summarized as demodulation, denoising and feature parameter extraction. Only these three issues are well solved, rolling bearing fault can be accurately diagnosed.2. A bearing fault diagnosis method based on EMD (empirical mode decomposition) denoising and spectral kurtosis is proposed. EMD denoising is summarized as two methods, i.e., threshold-based processing and filter-based processing. In threshold-based method, wavelet threshold-denoising is referenced; in filter-based method, two criterions are put forward according to the characteristic of faulty bearing vibration signal. Taking a simulated signal of faulty bearing as an example, the performance of the two methods is compared. As a result, filter-based EMD denoising method is more suitable to be a preprocessing means for bearing signal. In view of the difficulty to determine the band-pass filter parameters of resonance demodulation, the theory of spectral kurtosis is introduced. The frequency band with largest spectral kurtosis value is confirmed as the resonance frequency band. Combining EMD denoising and spectral kurtosis theory, a rolling bearing fault diagnosis method is presented, which is verified by practical engineering signal.3. Based on optimal Morlet wavelet filter and autocorrelation enhancement algorithm, an automatic diagnosis method of rolling bearing fault is proposed. To eliminate the frequency associated with interferential vibrations, the bearing signal is filtered with a band-pass filter determined by a Morlet wavelet whose parameters are optimized by genetic algorithm. The objective function and constraints are also discussed. Using the optimal Morlet wavelet filter, the SNR is improved significantly. To further reduce the residual in-band noise and highlight the periodic impact characteristic, an autocorrelation enhancement algorithm is proposed, which is then used to the filtered signal. In the enhanced autocorrelation envelope power spectrum, only several single spectrum lines would be left, which is corresponding to bearing fault frequency for a defective bearing or to shaft rotational frequency for normal bearing. It is very simple for operator to identify the bearing fault type. The proposed method can be conducted in an almost automatic way. The results from simulated and practical experiments prove the method is very effective for bearing faults diagnosis.4. A denoise method based on hidden Markov tree (HMT) model in dual tree complex wavelet domain is proposed. In view of the drawback of discrete wavelet transform with shift sensitivity and complex wavelet transform without perfect reconstruction, dual tree complex wavelet transform (DTCWT) is adopted to process the bearing signal. In view of the unfavorable effect of traditional wavelet denoise without considering the correlation between wavelet coefficients and the non-Gaussian nature of these coefficients, a denoise ideal of wavelet domain HMT model is introduced. Combining DTCWT and wavelet domain HMT model, a more advantage method is raised. According to the principle that the real and imaginary parts of DTCWT coefficients are considered simultaneously or separately, two kinds of denoise method based on dual tree complex wavelet domain hidden Markov tree model are proposed. Their performance are compared with traditional methods and indicated the simultaneously considered one is better. This method is then used in practical rolling bearing fault diagnosis to verify its efficiency.5. A rolling bearing fault diagnosis and prognosis method based on wavelet packet sample entropy is proposed. Sample entropy (SE) in information theory is introduced into the field of mechanical fault diagnosis. Its performance and the choice of calculation parameters are discussed, which indicated that SE has better performance than approximate entropy in term of anti-noise, sample length requirement, or reflecting the system nature. Since the greater the sub-band energy of wavelet packet decomposition is, the more obvious the fault information is, wavelet packet sample entropy (WPSE) is proposed, which can distinguish well bearing fault type. Then WPSE is applied to bearing fault trend prediction. Calculating the WPSE of life cycle bearing test rig data and using EMD to extract tendency, the bearing running states can be well predicted. WPSE can forecast earlier fault development fault than RMS and kurtosis, which indicate WPSE is a good bearing monitoring and forecasting tool.6. Based on LabView and Matlab software, an analysis system of rolling bearing vibration signal is developed. The system development environment and overall structure design are introduced. Then the implement method of each module is discussed. Through the function demo for actual signal, the convience and efficience of the system is verified.更多还原