强噪声背景下机械设备微弱信号的提取与检测技术研究
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摘要
在实际的机械设备故障诊断中,由于现场环境比较恶劣,所测得的振动信号除包含有用的特征信息外还存在大量的噪声干扰。尤其是机械设备的早期故障,特征信号比较微弱,往往被强噪声所淹没,极大地影响了设备状态信息的准确获取。因此,论文以机械设备为对象,研究了强噪声背景下微弱信号的提取和检测技术。
设备工况的变化以及其自身的非线性使机械设备的动态信号表现出非平稳性。经验模式分解方法是一种处理非线性非平稳信号的有效工具,但是对于强噪声背景下的微弱信号,噪声干扰会加重经验模式分解的边界效应,影响分解的质量和效果。为此,本文提出一种基于级联双稳随机共振降噪的经验模式分解方法,利用随机共振在微弱信号检测方面的优势,实现对微弱非平稳信号的提取。仿真实验以及滚动轴承的故障诊断实例验证了该方法的有效性。
奇异值分解是一种非线性滤波方法,广泛应用于信号的消噪和检测工作中。但是传统的奇异值分解通常是在时域中进行,由于奇异值对噪声比较敏感,因此奇异值分解只适用于弱噪声的情况。针对这个问题,论文提出了基于频域的奇异值分解方法,通过傅立叶变换将时域信号转换到频域进行处理,增强了奇异值分解的抗噪能力。此外,针对单层奇异值分解降噪效果的有限性,论文又提出了级联奇异值分解方法。对于强噪声背景下的微弱信号,通过级联的形式,实现噪声的逐级滤除。仿真实验表明该方法可以有效提高信噪比,实现微弱信号的检测。论文还分析了大信号干扰下的微弱信号识别问题。从剔除大信号干扰、分离微弱信号的角度,提出了基于独立分量分析的信噪分离方法,并通过一个转轴的偏心故障实例表明该方法具有良好的工程应用前景。此外,从减少频率泄露、增强微弱信号的角度,将多窗谱分析成功用于早期油膜涡动的故障诊断中。
为了满足不同厂矿企业的要求,同时作为本课题关键技术的载体,论文设计开发了一款功能完善、价格低廉的便携式数采分析仪,该仪器采用双CPU的硬件构架并以Linux操作系统为平台进行软件设计,可以实现高速多任务的实时采集。此外,仪器还配有多种通信接口,方便与上位机进行测点信息的下载和采集数据的上传。
In actual fault diagnosis of mechanical equipment, the measured vibration signals which for bad environment, not only contain useful features but also have much noise. Especially for incipient fault of mechanical equipment, its the feature is weak and usually submerged in heavy noise, so it is hard to be extracted. Aiming at mechanical equipment, this dissertation focuses on weak signal extraction and detection in heavy noise.
The dynamic signals of mechanical equipment often possess nonstationarities due to variance of operation and inherent nonlinearity of equipment. Empirical mode decomposition (EMD) method is an efficient tool for the nonlinear and nonstationary signals analysis. But EMD method have inherent drawback called end effect, and this drawback will be more serious when weak signal in heavy noise. For this prolem, this dissertation puts forward a new method whivh uses cascaded bistable stochastic resonance system (CBSRS) for noisy EMD. The successful application of fault diagnosis rolling bearing shows the method’s efficiency.
Singularity value decomposition (SVD) as a nonlinear filter method is widely used for signal de-noising and detection. But the traditional SVD method is usually in time domain and the singularity values are sensitive to noise, so SVD only can process signals mixed weak noise. For this reason, an improved method called frequency domain SVD is proposed. In addition, as the de-noising ability of single SVD system is limited, another novel method of cascaded SVD system (CSVD) is presented in this dissertation. Simulation experiments prove these two methods’feasibility.
Weak signal detection when submerged by large amplitude ones is analysed, and two method based on different point of view are proposed. One is based on signal and noise separation of independent component analysis (SNICA) method to eliminate large amplitude signal and extract weak feature ones. The other is based on multi-taper technique in order to reduce frequency leakage and enhance weak signals, Satisfactory results have been achieved when using them respectively to an axis eccentricity fault diagnosis and the oil whirl in early stage monitoring.
In order to meet the requirement of different applications and as the carrier of the key technology, an innovative portable fault diagnosis system with its advantages of high performance and low coast is presented. This system adapts dual CPU hardware structure and uses Linux as software development platform. In addition, it contains many communication interfaces to realize information transmission between portable instrument and upper condition monitoring system.
设备工况的变化以及其自身的非线性使机械设备的动态信号表现出非平稳性。经验模式分解方法是一种处理非线性非平稳信号的有效工具,但是对于强噪声背景下的微弱信号,噪声干扰会加重经验模式分解的边界效应,影响分解的质量和效果。为此,本文提出一种基于级联双稳随机共振降噪的经验模式分解方法,利用随机共振在微弱信号检测方面的优势,实现对微弱非平稳信号的提取。仿真实验以及滚动轴承的故障诊断实例验证了该方法的有效性。
奇异值分解是一种非线性滤波方法,广泛应用于信号的消噪和检测工作中。但是传统的奇异值分解通常是在时域中进行,由于奇异值对噪声比较敏感,因此奇异值分解只适用于弱噪声的情况。针对这个问题,论文提出了基于频域的奇异值分解方法,通过傅立叶变换将时域信号转换到频域进行处理,增强了奇异值分解的抗噪能力。此外,针对单层奇异值分解降噪效果的有限性,论文又提出了级联奇异值分解方法。对于强噪声背景下的微弱信号,通过级联的形式,实现噪声的逐级滤除。仿真实验表明该方法可以有效提高信噪比,实现微弱信号的检测。论文还分析了大信号干扰下的微弱信号识别问题。从剔除大信号干扰、分离微弱信号的角度,提出了基于独立分量分析的信噪分离方法,并通过一个转轴的偏心故障实例表明该方法具有良好的工程应用前景。此外,从减少频率泄露、增强微弱信号的角度,将多窗谱分析成功用于早期油膜涡动的故障诊断中。
为了满足不同厂矿企业的要求,同时作为本课题关键技术的载体,论文设计开发了一款功能完善、价格低廉的便携式数采分析仪,该仪器采用双CPU的硬件构架并以Linux操作系统为平台进行软件设计,可以实现高速多任务的实时采集。此外,仪器还配有多种通信接口,方便与上位机进行测点信息的下载和采集数据的上传。
In actual fault diagnosis of mechanical equipment, the measured vibration signals which for bad environment, not only contain useful features but also have much noise. Especially for incipient fault of mechanical equipment, its the feature is weak and usually submerged in heavy noise, so it is hard to be extracted. Aiming at mechanical equipment, this dissertation focuses on weak signal extraction and detection in heavy noise.
The dynamic signals of mechanical equipment often possess nonstationarities due to variance of operation and inherent nonlinearity of equipment. Empirical mode decomposition (EMD) method is an efficient tool for the nonlinear and nonstationary signals analysis. But EMD method have inherent drawback called end effect, and this drawback will be more serious when weak signal in heavy noise. For this prolem, this dissertation puts forward a new method whivh uses cascaded bistable stochastic resonance system (CBSRS) for noisy EMD. The successful application of fault diagnosis rolling bearing shows the method’s efficiency.
Singularity value decomposition (SVD) as a nonlinear filter method is widely used for signal de-noising and detection. But the traditional SVD method is usually in time domain and the singularity values are sensitive to noise, so SVD only can process signals mixed weak noise. For this reason, an improved method called frequency domain SVD is proposed. In addition, as the de-noising ability of single SVD system is limited, another novel method of cascaded SVD system (CSVD) is presented in this dissertation. Simulation experiments prove these two methods’feasibility.
Weak signal detection when submerged by large amplitude ones is analysed, and two method based on different point of view are proposed. One is based on signal and noise separation of independent component analysis (SNICA) method to eliminate large amplitude signal and extract weak feature ones. The other is based on multi-taper technique in order to reduce frequency leakage and enhance weak signals, Satisfactory results have been achieved when using them respectively to an axis eccentricity fault diagnosis and the oil whirl in early stage monitoring.
In order to meet the requirement of different applications and as the carrier of the key technology, an innovative portable fault diagnosis system with its advantages of high performance and low coast is presented. This system adapts dual CPU hardware structure and uses Linux as software development platform. In addition, it contains many communication interfaces to realize information transmission between portable instrument and upper condition monitoring system.
引文
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