机械设备早期故障预示中的微弱信号检测技术研究
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摘要
早期故障具有两方面含义,一是指处于早期阶段的故障、微弱故障或潜在故障;二是从物理意义上讲,某一故障是另一故障的早期阶段。故障发现得越早,越有助于设备的安全可靠运行。但是,设备早期故障的特征信号很微弱,往往被强噪声所淹没,信噪比很低,极大地影响了设备运行状态信息的准确获取。论文以机械设备为对象,研究了早期故障预示中的微弱信号检测与实用诊断技术。
针对传统的绝热近似小参数随机共振难以满足工程实际大参数条件下的微弱信号检测问题,本文提出了变步长随机共振数值算法。在深入分析近似熵用于度量信号复杂性性质的基础上,本文提出了基于近似熵测度的自适应随机共振方法,解决了限制随机共振在工程实际中推广使用的参数调节问题。金属车削过程的振动信号分析和滚动轴承故障诊断的成功应用表明上述方法的有效性。
基于混沌振子的微弱信号检测是通过“观察”待测信号加入后振子是否发生相变来实现的,但是这种“观察”缺少一个衡量标准,具有一定的主观性。尤其当噪声很强时,这种“目测”振子状态的办法就会失效。本文突破近似熵仅用于描述一维信号复杂度的局限性,提出了适合度量混沌振子二维相图的二维近似熵概念。在此基础上,本文提出了基于混沌振子和二维近似熵的微弱信号检测方法,并将其应用于旋转机械的状态监测和滚动轴承的故障诊断,取得了很好的效果。
工程信号中无效分量的干扰会使得微弱信号检测显得异常困难。独立分量分析可以从实测信号中分离出各个独立的源信号,是一种有效的微弱信号检测方法。针对混合信号时间延迟(或相位差)和噪声干扰对独立分量分析结果的影响问题,本文提出一种故障源信号的频域盲分离方法。涡流传感器失效检测和转子早期碰磨故障的成功诊断表明该方法广阔的应用前景。
支持向量数据描述是一种新的单值分类方法,能够只利用一类学习样本(或正常状态样本)建立分类器,其应用有望解决制约设备早期故障预示向智能化方向发展的故障数据缺乏问题。本文提出一种基于经验模式分解和支持向量数据描述的设备早期故障混合智能预示方法,并将其应用于滚动轴承和齿轮箱故障的智能诊断,取得了很好的效果。
作为本课题关键技术的载体,本文总结了作者在开发基于LabVIEW的远程监测诊断系统过程中运用的一些实用技术,提出了基于频域积分的振动参量转换修正算法,为设备动态信息的完整性和准确性提供了技术上的支持和保障。
Incipient fault of mechanical equipment contains two meanings: one means early fault, faint fault or latent fault, another means that a kind of fault is the early stage of the other kind. It is helpful for the equipment’s reliably working if we detect the fault in its early stage. Because the feature of the incipient fault is weak and usually submerged in heavy noise, it is difficult to be extracted. Aiming at mechanical equipment, this dissertation focuses on weak signal detection and practical diagnosis techniques in incipient fault prognosis.
Traditional adiabatic elimination stochastic resonance (SR) in small parameters is not adapt to engineering weak signal detection in large parameters, so a new numerical method called the step-changed SR is proposed. The properties of approximate entropy (ApEn) in signal complexity measure is analysed, and a novel adaptive SR method based on ApEn measurement is presented. It can solve the problem of parameter adjustment in SR. The successful application of vibration analysis of metal cutting and fault diagnosis of rolling bearings show the methods’efficiency.
Weak periodic signals can be detected by identifying the transformation of the chaotic oscillator from the chaotic state to the large-scale periodic state when the external signal is applied. We usually judge the change of chaotic oscillator only by our eyes, and there is not an objective criterion. Two-dimensional ApEn proposed in this dissertation has been proved to be an effective measure of the states of chaotic oscillator. A new weak signal detection method based on chaotic oscillator and two-dimensional ApEn is presented. Satisfactory results have been achieved when using this method to the rotating machinery condition monitoring and rolling bearings fault diagnosis.
Useless components in engineering signal usually lead weak feature extraction to be difficult. Independent component analysis (ICA) is an effective weak signal detection method, and it can separate the source component which is statistically independent from the mixed signals. But the capacity of ICA is usually effected by the phase difference and noise of the mixed signals. For this reason, an improved method called frequency domain blind source separation (FDBSS) is proposed. Successful applications of FDBSS are achieved in the detection of eddy-current sensor failure and the diagnosis of incipient impact-rub fault. The results show that FDBSS has widely prospect for application in the condition monitoring and fault diagnosis of mechanical equipment.
Support vector data description (SVDD) is a new one-class classification method. It can build a classifier with only one class data (or normal samples). So the application of SVDD to the machine fault diagnosis is expected to solve the problem of the shortage of incipient fault data in intelligent diagnosis. A hybrid intelligent prognosis method based on empirical mode decomposition and SVDD is proposed, and it is applied to fault diagnosis of rolling bearings and gearbox. The results show that the presented method is efficient to extract the fault feature, reduce the dimension of the signals and improve the veracity of one-class classification in intelligent diagnosis significantly.
As the carrier of the key technology, some practical techniques of the development of remote monitoring and diagnosis system based on LabVIEW are summarized. A simple effective modification algorithm for the vibration signals integration in frequency domain is presented. This algorithm provides the technical support for the integrality and accuracy of equipment dynamic information.
针对传统的绝热近似小参数随机共振难以满足工程实际大参数条件下的微弱信号检测问题,本文提出了变步长随机共振数值算法。在深入分析近似熵用于度量信号复杂性性质的基础上,本文提出了基于近似熵测度的自适应随机共振方法,解决了限制随机共振在工程实际中推广使用的参数调节问题。金属车削过程的振动信号分析和滚动轴承故障诊断的成功应用表明上述方法的有效性。
基于混沌振子的微弱信号检测是通过“观察”待测信号加入后振子是否发生相变来实现的,但是这种“观察”缺少一个衡量标准,具有一定的主观性。尤其当噪声很强时,这种“目测”振子状态的办法就会失效。本文突破近似熵仅用于描述一维信号复杂度的局限性,提出了适合度量混沌振子二维相图的二维近似熵概念。在此基础上,本文提出了基于混沌振子和二维近似熵的微弱信号检测方法,并将其应用于旋转机械的状态监测和滚动轴承的故障诊断,取得了很好的效果。
工程信号中无效分量的干扰会使得微弱信号检测显得异常困难。独立分量分析可以从实测信号中分离出各个独立的源信号,是一种有效的微弱信号检测方法。针对混合信号时间延迟(或相位差)和噪声干扰对独立分量分析结果的影响问题,本文提出一种故障源信号的频域盲分离方法。涡流传感器失效检测和转子早期碰磨故障的成功诊断表明该方法广阔的应用前景。
支持向量数据描述是一种新的单值分类方法,能够只利用一类学习样本(或正常状态样本)建立分类器,其应用有望解决制约设备早期故障预示向智能化方向发展的故障数据缺乏问题。本文提出一种基于经验模式分解和支持向量数据描述的设备早期故障混合智能预示方法,并将其应用于滚动轴承和齿轮箱故障的智能诊断,取得了很好的效果。
作为本课题关键技术的载体,本文总结了作者在开发基于LabVIEW的远程监测诊断系统过程中运用的一些实用技术,提出了基于频域积分的振动参量转换修正算法,为设备动态信息的完整性和准确性提供了技术上的支持和保障。
Incipient fault of mechanical equipment contains two meanings: one means early fault, faint fault or latent fault, another means that a kind of fault is the early stage of the other kind. It is helpful for the equipment’s reliably working if we detect the fault in its early stage. Because the feature of the incipient fault is weak and usually submerged in heavy noise, it is difficult to be extracted. Aiming at mechanical equipment, this dissertation focuses on weak signal detection and practical diagnosis techniques in incipient fault prognosis.
Traditional adiabatic elimination stochastic resonance (SR) in small parameters is not adapt to engineering weak signal detection in large parameters, so a new numerical method called the step-changed SR is proposed. The properties of approximate entropy (ApEn) in signal complexity measure is analysed, and a novel adaptive SR method based on ApEn measurement is presented. It can solve the problem of parameter adjustment in SR. The successful application of vibration analysis of metal cutting and fault diagnosis of rolling bearings show the methods’efficiency.
Weak periodic signals can be detected by identifying the transformation of the chaotic oscillator from the chaotic state to the large-scale periodic state when the external signal is applied. We usually judge the change of chaotic oscillator only by our eyes, and there is not an objective criterion. Two-dimensional ApEn proposed in this dissertation has been proved to be an effective measure of the states of chaotic oscillator. A new weak signal detection method based on chaotic oscillator and two-dimensional ApEn is presented. Satisfactory results have been achieved when using this method to the rotating machinery condition monitoring and rolling bearings fault diagnosis.
Useless components in engineering signal usually lead weak feature extraction to be difficult. Independent component analysis (ICA) is an effective weak signal detection method, and it can separate the source component which is statistically independent from the mixed signals. But the capacity of ICA is usually effected by the phase difference and noise of the mixed signals. For this reason, an improved method called frequency domain blind source separation (FDBSS) is proposed. Successful applications of FDBSS are achieved in the detection of eddy-current sensor failure and the diagnosis of incipient impact-rub fault. The results show that FDBSS has widely prospect for application in the condition monitoring and fault diagnosis of mechanical equipment.
Support vector data description (SVDD) is a new one-class classification method. It can build a classifier with only one class data (or normal samples). So the application of SVDD to the machine fault diagnosis is expected to solve the problem of the shortage of incipient fault data in intelligent diagnosis. A hybrid intelligent prognosis method based on empirical mode decomposition and SVDD is proposed, and it is applied to fault diagnosis of rolling bearings and gearbox. The results show that the presented method is efficient to extract the fault feature, reduce the dimension of the signals and improve the veracity of one-class classification in intelligent diagnosis significantly.
As the carrier of the key technology, some practical techniques of the development of remote monitoring and diagnosis system based on LabVIEW are summarized. A simple effective modification algorithm for the vibration signals integration in frequency domain is presented. This algorithm provides the technical support for the integrality and accuracy of equipment dynamic information.
引文
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