共查询到17条相似文献,搜索用时 171 毫秒
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针对滚动轴承故障振动信号非平稳的特征,以及传统傅里叶变换不能反映信号细节的缺陷,引入了一种基于本征模态函数包络谱的方法。首先,采用经验模态分解(empirical mode decomposition,EMD)将滚动轴承故障振动信号分解成若干个本征模态函数(intrinsic mode function,IMF)之和;然后,求出包含主要信息成分的IMF分量的Hilbert包络谱;最后,对照滚动轴承故障特征频率,进而判定故障类型。通过对滚动轴承内圈、外圈故障振动信号的分析处理,表明该方法能有效地提取滚动轴承的故障特征。 相似文献
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基于EMD与功率谱分析的滚动轴承故障诊断方法研究 总被引:7,自引:0,他引:7
针对西部油田大型设备故障信号的非线性、非平稳特征,提出一种基于经验模态分解方法EMD(empirical mode decomposition)和功率谱的分析方法。首先对滚动轴承振动信号进行经验模态分解,然后对分解后包含轴承故障特征信息的固有模态函数分量作功率谱分析,得到各分量的功率谱图,清晰直观显示出故障特征信号的功率谱,从混有背景信号和噪声的振动信号中提取轴承故障信息。由于EMD方法具有自适应特性,适宜于非线性、非平稳信号的分解,该方法应用于滚动轴承的故障振动信号分析中,结果表明,该方法能够突出滚动轴承振动信号的故障特征,从而提高滚动轴承故障诊断的准确性。 相似文献
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针对滚动轴承振动信号的特点,提出一种基于集合经验模态分解的滚动轴承振动信号希尔伯特谱分析方法。这一方法采用集合经验模态分解对滚动轴承振动信号进行分解,可以抑制传统经验模态分解可能产生的模态混淆现象。对于所得到的所有固有模态函数,采用相关因数法自动获取与原信号相关程度大的固有模态函数分量,并进行希尔伯特变换,计算瞬时频率和幅值,进而得到振动信号的时间、频率和幅值三维希尔伯特谱。通过对滚动轴承仿真信号与实际振动信号进行试验研究,验证了利用基于集合经验模态分解的滚动轴承振动信号希尔伯特谱分析方法进行故障诊断的有效性。 相似文献
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针对变转速滚动轴承故障特征提取较难的问题,提出一种基于参数优化变分模态分解(parameter optimized variational mode decomposition,简称POVMD)与包络阶次谱的变工况滚动轴承故障诊断方法。首先,采用POVMD对变转速滚动轴承振动信号进行分解,得到若干个本征模态函数之和;其次,对各个分量的时域信号进行角域重采样,将时变信号转化为平稳信号处理,再利用Hilbert变换估计重采样后的平稳信号的包络;最后,对得到的包络信号进行阶比分析,从谱图中读取故障特征信息。将POVMD方法与经验模态分解进行了对比,仿真信号分析结果表明了POVMD方法的优越性。将提出的变转速滚动轴承故障诊断方法应用于试验数据分析,分析结果表明,所提出的方法能够实现变转速滚动轴承的故障诊断,而且诊断效果优于现有方法。 相似文献
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《机械传动》2017,(4):176-180
针对变转速条件下滚动轴承故障特征难以提取的问题,提出了一种基于角域经验小波变换的变转速滚动轴承故障诊断方法。该方法首先利用等角度重采样将变转速下非平稳的滚动轴承故障振动信号转化为角域平稳信号,然后应用经验小波变换(Empirical mode decomposition,EWT)对角域平稳信号进行自适应分解,得到若干个经验模态分量,最后选择峭度值最大的经验模态分量进行包络谱分析,提取出滚动轴承故障的阶比特征。为提高经验小波变换的分解效率,对其频谱分割方法进行了改进。滚动轴承故障诊断实例表明,该方法能够有效地抑制噪声等干扰成分的影响,精确提取滚动轴承故障的阶比特征,为变转速条件下的滚动轴承故障诊断提供一种有效方法。 相似文献
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针对传统阈值去噪方法在处理轴承故障信号时存在的不足,提出了基于小波改进阈值去噪与经验模态分解(Empirical Mode Decomposition,EMD)的滚动轴承故障信号的分析方法。为改善小波去噪产生的信号振荡和失真问题,构造了适用于滚动轴承振动信号的非线性阈值函数,并将其用为滚动轴承故障信号的噪声过滤器。采用经验模态分解将去噪后的信号分解成若干固有模态函数(Intrinsic Mode Function,IMF),并用统计分析的方法提取出谱峭度值、各固有模态函数与去噪信号之间的互相关系数最大的分量。最后,为了在频域内提取到故障特征频率,对抽取到的固有模态分量进行包络分析。仿真数据分析和模拟实验数据分析表明,所提方法可有效地提取轴承故障特征频率,实现轴承的故障诊断。 相似文献
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A fault diagnosis approach for roller bearing based on IMF envelope spectrum and SVM 总被引:1,自引:0,他引:1
Targeting the modulation characteristics of roller bearing fault vibration signals, a method of fault feature extraction based on intrinsic mode function (IMF) envelope spectrum is proposed to overcome the limitations of conventional envelope analysis method. By utilizing the proposed feature extraction method, the disadvantages of conventional envelope analysis method such as the chosen of central frequency of filter with experience in advance, looking for spectral line of fault characteristic frequencies in envelope spectrum and so on could be overcome. Firstly, the original modulation signals are decomposed into a number of IMFs by empirical mode decomposition (EMD) method. Secondly, the ratios of amplitudes at the different fault characteristic frequencies in the envelope spectra of some IMFs that include dominant fault information are defined as the characteristic amplitude ratios. Finally, the characteristic amplitude ratios serve as the fault characteristic vectors to be input to the support vector machine (SVM) classifiers and the work condition and fault patterns of the roller bearings are identified. Since the recognition results are available directly from the output of the SVM classifiers, the proposed diagnosis method provides the possibility to fulfill the automatic recognition to machinery faults. 相似文献
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基于经验模态分解的滚动轴承故障诊断方法 总被引:13,自引:1,他引:13
提出了一种基于经验模态分解的滚动轴承故障诊断方法,并定义了能量熵的概念。从不同状态的滚动轴承振动信号的能量熵值中发现,当滚动轴承发生故障时,各频带的能量会发生变化。为了进一步对滚动轴承的状态和故障类型进行分类,再从若干个包含主要故障信息的IMF分量中提取能量特征参数作为神经网络的输入参数来识别滚动轴承的故障类型。对滚动轴承的正常状态、内圈故障和外圈故障振动信号的分析结果表明,以经验模态分解为预处理器提取各频带能量作为特征参数的神经网络诊断方法比以小波包分析为预处理器的神经网络诊断方法有更高的故障识别率,可以准确、有效地识别滚动轴承的工作状态和故障类别。 相似文献
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FAULT DIAGNOSIS APPROACH FOR ROLLER BEARINGS BASED ON EMPIRICAL MODE DECOMPOSITION METHOD AND HILBERT TRANSFORM 总被引:2,自引:0,他引:2
Yu Dejie Cheng Junsheng Yang Yu College of Mechanical Automotive Engineering Hunan University Changsha China 《机械工程学报(英文版)》2005,18(2):267-270
Based upon empirical mode decomposition (EMD) method and Hilbert spectrum, a method for fault diagnosis of roller bearing is proposed. The orthogonal wavelet bases are used to translate vibration signals of a roller bearing into time-scale representation, then, an envelope signal can be obtained by envelope spectrum analysis of wavelet coefficients of high scales. By applying EMD method and Hilbert transform to the envelope signal, we can get the local Hilbert marginal spectrum from which the faults in a roller bearing can be diagnosed and fault patterns can be identified. Practical vibration signals measured from roller bearings with out-race faults or inner-race faults are analyzed by the proposed method. The results show that the proposed method is superior to the traditional envelope spectrum method in extracting the fault characteristics of roller bearings. 相似文献
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提出一种基于内禀模态函数(Intrinsic Mode Function,IMF)、自回归(Auto-Regressive,AR)模型和关联维数的滚动轴承故障诊断方法.该方法首先采用经验模态分解(Empirical Mode Decomposition,EMD)将滚动轴承振动信号分解成若干个IMF,然后对包含主要故障信息的IMF分量建立AR模型,计算AR模型自回归参数的关联维数,并以关联维数作为特征向量输入神经网络分类器,最后通过网络的输出结果来识别轴承的工作状态和故障类型.对实验数据的分析结果表明,该方法能有效地应用于滚动轴承的故障诊断. 相似文献
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Gear vibration signals always display non-stationary behavior. HHT (Hilbert–Huang transform) is a method for adaptive analysis of non-linear and non-stationary signals, but it can only distinguish conspicuous faults. SOM (self-organizing feature map) neural network is a network learning with no instructors which has self-adaptive and self-learning features and can compensate for the disadvantage of HHT. This paper proposed a new gear fault identification method based on HHT and SOM neural network. Firstly, the frequency families of gear vibration signals were separated effectively by EMD (empirical mode decomposition). Then Hilbert spectrum and Hilbert marginal spectrum were obtained by Hilbert transform of IMFs (intrinsic mode functions). The amplitude changes of gear vibration signals along with time and frequency had been displayed respectively. After HHT, the energy percentage of the first six IMFs were chosen as input vectors of SOM neural network for fault classification. The analysis results showed that the fault features of these signals can be accurately extracted and distinguished with the proposed approach. 相似文献
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Bearing fault detection and diagnosis based on order tracking and Teager-Huang transform 总被引:1,自引:0,他引:1
The vibration signal of the run-up or run-down process is more complex than that of the stationary process. A novel approach
to fault diagnosis of roller bearing under run-up condition based on order tracking and Teager-Huang transform (THT) is presented.
This method is based on order tracking, empirical mode decomposition (EMD) and Teager Kaiser energy operator (TKEO) technique.
The nonstationary vibration signals are transformed from the time domain transient signal to angle domain stationary one using
order tracking. EMD can adaptively decompose the vibration signal into a series of zero mean amplitude modulation-frequency
modulation (AM-FM) intrinsic mode functions (IMFs). TKEO can track the instantaneous amplitude and instantaneous frequency
of the AM-FM component at any instant. Experimental examples are conducted to evaluate the effectiveness of the proposed approach.
The experimental results provide strong evidence that the performance of the Teager-Huang transform approach is better to
that of the Hilbert-Huang transform approach for bearing fault detection and diagnosis. The Teager-Huang transform has better
resolution than that of Hilbert-Huang transform. Teager-Huang transform can effectively diagnose the faults of the bearing,
thus providing a viable processing tool for gearbox defect monitoring. 相似文献