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基于HOC的故障诊断虚拟仪器系统研究 总被引:1,自引:1,他引:1
机械故障诊断仍是当今研究的一个热点,虚拟仪器技术的迅速发展,将故障诊断与电子测试技术结合起来,这是现代故障诊断技术发展的重要趋势.本文介绍了现代信号处理理论高阶累积量(HOC),并将其引入到虚拟仪器的信号分析处理之中,对机械齿轮传动系统的故障进行了特征提取和分析,以VC 软件开发平台为基础,结合MATLAB语言研制和开发出了机械故障诊断的虚拟仪器系统,设计了各种功能模块,包括机械振动信号的采集、分析处理和机械系统故障诊断等,实现了基于HOC故障诊断的虚拟仪器系统. 相似文献
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齿轮传动系统振动信号带有明显的非平稳性,齿轮断齿故障使非平稳特征更加明显,并伴有明显的脉冲冲击特性。利用小波分析处理非平稳的优势,结合齿轮传动系统的振动特性和Morlet小波能够处理脉冲信号的特点,建立了基于Morlet小波的时频分析方法,通过对某齿轮增速箱齿轮断齿故障的诊断,证明本方法不但能够准确诊断出断齿故障,而且能够评估断齿数量,较好地弥补了传统频谱分析的不足。研究结果对于齿轮断齿等具有脉冲信号特征的故障诊断具有一定的指导意义。 相似文献
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介绍了小波分析用于信号处理的基本思想及在机械故障诊断中的研究现状;针对提升机出现的非平稳振动故障,应用小波分析理论通过正交小波包变换,将信号分解到不同的频带内,实现了信噪分离,提取了提升机的振动故障信息。研究证明采用小波分析对提升机的非平稳振动进行深层次分析,可有效诊断出设备的故障,是处理非平稳信号的有力工具。 相似文献
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振动信号处理与特征参数提取是实现齿轮智能故障诊断的关键。提出采用形态梯度算法对齿轮振动信号进行处理,既可以抑制噪声又可充分突出故障信号的冲击特征,能够在强噪声背景下有效地提取振动信号中反映齿轮工作状态的有用分量;在此基础上提出采用非负矩阵分解的特征提取方法对信号进行压缩,计算用于齿轮故障诊断的特征参量。结果表明,与传统的信号处理与特征参量提取方法相比,笔者提出的方法能够具有更高的分类精度,为准确判断齿轮工作状态提供了一种行之有效的新方法。 相似文献
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The generalized demodulation time–frequency analysis is a novel signal processing method, which is particularly suitable for the processing of multi-component amplitude-modulated and frequency-modulated (AM–FM) signals as it can decompose a multi-component signal into a set of single-component signals whose instantaneous frequencies own physical meaning. While fault occurs in gear, the vibration signals measured from gearbox would exactly display AM–FM characteristics. Therefore, targeting the modulation feature of gear vibration signal in run-ups and run-downs, a fault diagnosis method in which generalized demodulation time–frequency analysis and envelope order spectrum technique are combined is put forward and applied to the transient analysis of gear vibration signal. Firstly the multi-component vibration signal of gear is decomposed into some mono-component signals using the generalized demodulation time–frequency analysis approach; secondly the envelope analysis is performed to each single-component signal; thirdly each envelope signal is re-sampled in angle domain; finally the spectrum analysis is applied to each re-sampled signal and the corresponding envelope order spectrum can be obtained. Furthermore, the gear working condition can be identified according to the envelope order spectrum. The analysis results from the simulation and experimental signals show that the proposed algorithm was effective in gear fault diagnosis. 相似文献
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为了解决船用设备运行的可靠性这一问题,在工控机等硬件设备上开发了基于C#平台的船用机械故障诊断系统。首先,构建了工控机、信号调理器等硬件组成部分;在此基础上,采用C#编制了采集卡接口、信号处理方法、图形绘制等程序;最后进行了调试并应用于船用现场试验。结合齿轮故障设备模拟试验台,运用故障诊断系统采集信号,利用信号处理软件分析了正常齿轮与磨损齿轮的时域波形和频谱波形,分析了断裂齿轮的细化频谱的边频带,以此识别出了各类齿轮失效所在位置和原因。研究结果表明,船用机械设备故障诊断系统对于故障齿轮的检测具有有效性和可行性,同时该结果为利用故障诊断系统对轴承、柴油机等其他故障设备的诊断开发过程奠定了基础。 相似文献
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Hilbert-Huang变换在齿轮故障诊断中的应用 总被引:17,自引:3,他引:17
为齿轮故障诊断提供了一种新的途径,将Hilbert-Huang变换引入齿轮故障诊断,提出了局部Hilbert能量谱的概念,同时根据齿轮故障振动信号的特点建立了两种基于Hilbert-Huang变换的齿轮故障诊断方法:基于EMD的频率族分离法和Hilbert能量谱方法。采用EMD(Empiricalmodedecomposition)方法对齿轮振动信号能有效地将各个频率族分离;局部Hilbert能量谱可以反映齿轮振动信号的能量随时间和频率的分布情况,从而可以提取齿轮振动信号的故障信息。将这两种方法应用于齿轮故障诊断中,结果表明,基于EMD的频率族分离法和Hilbert能量谱方法都能有效地提取齿轮故障特征信息。 相似文献
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Wear detection in gear system using Hilbert-Huang transform 总被引:1,自引:0,他引:1
Fourier methods are not generally an appropriate approach in the investigation of faults signals with transient components.
This work presents the application of a new signal processing technique, the Hilbert-Huang transform and its marginal spectrum,
in analysis of vibration signals and faults diagnosis of gear. The Empirical mode decomposition (EMD), Hilbert-Huang transform
(HHT) and marginal spectrum are introduced. Firstly, the vibration signals are separated into several intrinsic mode functions
(IMFs) using EMD. Then the marginal spectrum of each IMF can be obtained. According to the marginal spectrum, the wear fault
of the gear can be detected and faults patterns can be identified. The results show that the proposed method may provide not
only an increase in the spectral resolution but also reliability for the faults diagnosis of the gear. 相似文献
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局部特征尺度分解方法及其在齿轮故障诊断中的应用 总被引:10,自引:0,他引:10
在定义瞬时频率具有物理意义的单分量信号——内禀尺度分量(Intrinsic scale component,ISC)的基础上,提出一种新的自适应信号分解方法——局部特征尺度分解(Local characteristic-scale decomposition,LCD)。LCD方法可以自适应地将任何一个复杂信号分解为若干个瞬时频率具有物理意义的ISC分量之和,非常适合于处理多分量的调幅—调频信号。当齿轮发生故障时,其振动信号一般为多分量的调幅—调频信号,因此局部特征尺度分解方法可以有效地应用于齿轮故障诊断。对LCD和经验模态分解(Empirical mode decomposition,EMD)、局部均值分解(Local mean decomposition,LMD)方法进行对比,结果表明了LCD方法的优越性。同时,针对齿轮故障振动信号的调制特征,将LCD方法和包络分析法相结合应用于齿轮故障诊断,对实际的齿轮故障振动信号进行分析,结果表明LCD方法可以有效地应用于齿轮故障诊断。 相似文献
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《Measurement》2014
The vibration signal of a gear system is selected as the original information of fault diagnosis and the gear system vibration equipment is established. The vibration acceleration signals of the normal gear, gear with tooth root crack fault, gear with pitch crack fault, gear with tooth wear fault and gear with multi-fault (tooth root crack & tooth wear fault) is collected in four kinds of speed conditions such as 300 rpm, 900 rpm, 1200 rpm and 1500 rpm. Using the method of wavelet threshold de-noising to denoise the original signal and decomposing the denoising signal utilizing the wavelet packet transform, then 16 frequency bands of decomposed signal are got. After restructuring the decomposing signal and obtaining the signal energy in each frequency band, the signal energy of the 16 bands is as the shortlisted fault characteristic data. Based on this, using the methods of principal component analysis (short for PCA) and kernel principal component analysis (short for KPCA) to extract the feature from the fault features of shortlisted 16-dimensional data feature, then the effect of reducing dimension analysis are compared. The fault classifications are displayed through the information that got from the first and the second principal component and kernel principal component, and these demonstrate they have a different and good effect of classification. Meanwhile, the article discusses the effect of feature extraction and classification that caused by the kernel function and the different options of its parameters. These provide a new method for a gear system fault feature extraction and classification. 相似文献