机电设备微弱特征提取与诊断方法研究
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摘要
机械设备自身结构的复杂性、多个零部件的协同工作、激励和故障源的多样性等都可能造成振动信号的复杂性,给故障诊断带来困难。本论文以机电设备为对象,对复杂振动信号中的微弱特征提取及相关诊断方法进行了研究。
为了提取淹没于噪声中的微弱特征信号,本文研究了级联双稳随机共振系统的非线性低通滤波特性。研究发现,通过级联这样一种方式,高频能量能不断地向低频转移,在实现低通滤波的同时,位于低频的微弱特征成分由于能量的增加将逐级突显出来。刀具切削和滚动轴承的诊断实例说明了其实用性。
每个传感器采集的信号往往是设备多个零部件振动源的混合信号,盲源分离技术为此类混合信号的分离提供了理论基础。由于实际测得振动信号中常掺杂有噪声,而目前的盲分离算法均没有考虑噪声的影响。本文提出一种基于随机共振与盲分离相结合的方法,该方法先对有噪混合信号进行级联双稳随机共振降噪再进行盲分离。仿真实验表明该方法可以有效地提高盲分离性能。
经验模式分解是一种根据信号局部特征进行自适应分解的时频分析方法,可以得到若干基本模式分量;支持向量机作为一种新型的机器学习方法,可以很好地用于模式分类当中。同时,由于不同的故障情况常呈现出不同的复杂性,本文提出一种Renyi熵复杂性测度下的基于经验模式分解和支持向量机的故障诊断方法,该方法将经验模式分解得到的若干基本模式分量的Renyi熵作为特征向量输入支持向量机进行训练、识别。滚动轴承的故障诊断实例说明了该种方法的应用前景。
终端的便携式数采分析仪实时、准确地采集设备状态数据是状态监测与故障诊断系统可靠运行的重要基础。本文介绍了一种写过滤保护机制下、基于嵌入式操作系统的便携式数采分析仪的开发方法,可以有效提高仪器的稳定性。由于传统的设备状态监测系统与设备管理系统缺少有效的通信机制,不利于设备的及时诊断与维护,本文对网络架构下面向设备管理的嵌入式监测系统的开发进行了相应的研究,实现了两者之间信息的有机融合。
The complexity of vibration signals may come from the complexity of equipment structure, cooperation of multi-parts, diversity of excitation and fault sources, which brings difficulties to fault diagnosis. Aiming at the electric- mechanical equipment, this dissertation focuses on the research on weak feature extraction from complex signals and related diagnosis methods.
The extraction of weak feature signal submerged in strong noise plays an important role in fault diagnosis. The non-linear filter characteristic of cascaded bistable stochastic resonance system (CBSRS) is revealed further in this dissertation. The results show that weak feature frequency components located in low frequency area can be extracted during the implementation of low-pass filter owing to the energy transfer mechanism from high frequency area to low frequency area by CBSRS. Metal cutting experiment and roller-bearings fault diagnosis examples demonstrate its practicability.
The signals collected by each sensor are always the mixtures of multi-vibration sources caused by the coupling of different mechanical parts. Blind source separation (BSS) and independent component analysis (ICA) provide solutions to the separation of different sources. Due to the facts that measurement mechanical vibration signals are always corrupted with addictive background noise and the negative impact of noise is ignored in the existing blind separation arithmetic, a method of CBSRS de-noising is presented and applied to the noisy ICA problem. A simulation experiment proves its feasibility.
Empirical mode decomposition (EMD) is a time-frequency analysis method and can adaptively decompose the signal into several intrinsic mode functions (IMFs) according to its characteristic time scale. Support vector machine (SVM) is a new machine learning method and can be taken as a classifier. According to the fact of different fault with different complexity, a diagnosis method, Renyi entropy regarded as complexity criterion, is put forward based-on EMD and SVM in this dissertation. The method takes the Renyi entropy of several IMFs as the characteristic vector and inputs them into SVM for training and recognition. A roller-bearings fault diagnosis example demonstrates its practicability.
The terminal portable instruments play a vital role in the condition monitoring and fault diagnosis system. The dissertation presents a portable data-acquisition and analysis instrument on the basis of embedded operating system (OS) with enhanced write filter (EWF), which can effectively enhance the instrument’s OS stability. Because of the lack of effective communication mechanism between conventional condition monitoring system and plant management system, an embedded condition monitoring system for plant management is proposed under internet framework, which realizes the information fusion of condition monitoring system and plant management system.
为了提取淹没于噪声中的微弱特征信号,本文研究了级联双稳随机共振系统的非线性低通滤波特性。研究发现,通过级联这样一种方式,高频能量能不断地向低频转移,在实现低通滤波的同时,位于低频的微弱特征成分由于能量的增加将逐级突显出来。刀具切削和滚动轴承的诊断实例说明了其实用性。
每个传感器采集的信号往往是设备多个零部件振动源的混合信号,盲源分离技术为此类混合信号的分离提供了理论基础。由于实际测得振动信号中常掺杂有噪声,而目前的盲分离算法均没有考虑噪声的影响。本文提出一种基于随机共振与盲分离相结合的方法,该方法先对有噪混合信号进行级联双稳随机共振降噪再进行盲分离。仿真实验表明该方法可以有效地提高盲分离性能。
经验模式分解是一种根据信号局部特征进行自适应分解的时频分析方法,可以得到若干基本模式分量;支持向量机作为一种新型的机器学习方法,可以很好地用于模式分类当中。同时,由于不同的故障情况常呈现出不同的复杂性,本文提出一种Renyi熵复杂性测度下的基于经验模式分解和支持向量机的故障诊断方法,该方法将经验模式分解得到的若干基本模式分量的Renyi熵作为特征向量输入支持向量机进行训练、识别。滚动轴承的故障诊断实例说明了该种方法的应用前景。
终端的便携式数采分析仪实时、准确地采集设备状态数据是状态监测与故障诊断系统可靠运行的重要基础。本文介绍了一种写过滤保护机制下、基于嵌入式操作系统的便携式数采分析仪的开发方法,可以有效提高仪器的稳定性。由于传统的设备状态监测系统与设备管理系统缺少有效的通信机制,不利于设备的及时诊断与维护,本文对网络架构下面向设备管理的嵌入式监测系统的开发进行了相应的研究,实现了两者之间信息的有机融合。
The complexity of vibration signals may come from the complexity of equipment structure, cooperation of multi-parts, diversity of excitation and fault sources, which brings difficulties to fault diagnosis. Aiming at the electric- mechanical equipment, this dissertation focuses on the research on weak feature extraction from complex signals and related diagnosis methods.
The extraction of weak feature signal submerged in strong noise plays an important role in fault diagnosis. The non-linear filter characteristic of cascaded bistable stochastic resonance system (CBSRS) is revealed further in this dissertation. The results show that weak feature frequency components located in low frequency area can be extracted during the implementation of low-pass filter owing to the energy transfer mechanism from high frequency area to low frequency area by CBSRS. Metal cutting experiment and roller-bearings fault diagnosis examples demonstrate its practicability.
The signals collected by each sensor are always the mixtures of multi-vibration sources caused by the coupling of different mechanical parts. Blind source separation (BSS) and independent component analysis (ICA) provide solutions to the separation of different sources. Due to the facts that measurement mechanical vibration signals are always corrupted with addictive background noise and the negative impact of noise is ignored in the existing blind separation arithmetic, a method of CBSRS de-noising is presented and applied to the noisy ICA problem. A simulation experiment proves its feasibility.
Empirical mode decomposition (EMD) is a time-frequency analysis method and can adaptively decompose the signal into several intrinsic mode functions (IMFs) according to its characteristic time scale. Support vector machine (SVM) is a new machine learning method and can be taken as a classifier. According to the fact of different fault with different complexity, a diagnosis method, Renyi entropy regarded as complexity criterion, is put forward based-on EMD and SVM in this dissertation. The method takes the Renyi entropy of several IMFs as the characteristic vector and inputs them into SVM for training and recognition. A roller-bearings fault diagnosis example demonstrates its practicability.
The terminal portable instruments play a vital role in the condition monitoring and fault diagnosis system. The dissertation presents a portable data-acquisition and analysis instrument on the basis of embedded operating system (OS) with enhanced write filter (EWF), which can effectively enhance the instrument’s OS stability. Because of the lack of effective communication mechanism between conventional condition monitoring system and plant management system, an embedded condition monitoring system for plant management is proposed under internet framework, which realizes the information fusion of condition monitoring system and plant management system.
引文
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