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《机械强度》2015,(5):816-822
针对滚动轴承故障微弱信号特征识别问题,提出一种非抽样运算的自适应冗余提升小波包诊断方法,解决了传统的小波包或提升小波变换进行抽样运算造成故障信息失真问题。该方法以提升原理为基础,通过Lagrange插值细分思想计算初始的非抽样预测和更新算子,进而构造了自适应冗余提升小波包分解与重构算法。对仿真信号进行降噪与抗频率混叠实验,结果表明,该方法降噪能力优于传统小波包,且不存在频率混叠现象。在异步电动机上实测了滚动轴承6205无故障、内圈故障、外圈故障及滚动体故障时的振动信号,用这种方法成功提取了各类故障的特征频率及倍频,且比传统小波包具有更高的诊断精度。 相似文献
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《装备制造技术》2017,(4)
针对经典小波包和双树复小波包(dual tree complex wavelet package transform,DTCWPT)能量泄漏和频率混叠的缺陷,提出完全抗混叠的DTCWPT改进算法,该算法解决了经典小波包存在负频率以及经典小波包和DTCWPT滤波器频率不完全截止问题。根据高斯白噪声频率充满整个频带的特性,通过小波包变换对高斯白噪声进行分解,利用频带能量泄漏的定量分析方法,验证了改进DTCWPT具有完全的抗频带能量泄漏特性。将改进DTCWPT方法和包络谱熵引入到轴承故障诊断中,该方法的核心是:对轴承振动信号进行改进DTCWPT变换得到不同尺度的分解信号,分别计算各分解信号的包络谱熵,合并熵值较小的几个分量信号的包络谱,最后根据合并的包络谱来检测轴承故障。该方法在消除经典小波包变换和DTCWPT频率混叠和能量泄漏的同时还解决了小波包分量选择盲目的问题。最后应用轴承故障试验数据对该方法进行试验验证,结果表明:改进DTCWPT结合包络谱熵选择的方法能够很好提取出轴承故障特征频率的基频、倍频,提高了轴承故障的诊断效果。 相似文献
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基于改进双树复小波变换的轴承多故障诊断 总被引:3,自引:0,他引:3
针对双树复小波变换产生频率混叠的缺陷,提出了改进双树复小波变换的轴承多故障诊断方法,该方法综合利用了双树复小波包变换和经验模态分解技术。首先,利用双树复小波变换将振动信号分解成不同频带的分量;然后,将各小波分量进行经验模态分解,获得各小波分量的主频率分量信号;最后,计算各小波分量的主频率分量信号的包络谱,根据包络谱识别齿轮箱轴承的故障部位和类型。通过仿真信号和齿轮箱轴承多故障振动实验信号的研究结果表明,该方法不仅消除了频率混叠现象,提高了信噪比和频带选择的正确性,而且提高了从强噪声环境中提取瞬态冲击特征的能力,能有效识别轴承的故障类型。 相似文献
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针对双树复小波变换分解层数需要先验确定和重构后各子带出现的频率混叠现象,提出了一种改进双树复小波变换的齿轮箱复合故障特征提取方法。首先,确定双树复小波变换的分解层数和有效的子带;对得到的各子带进行去频率混叠,确保消除频率混叠现象,使每个子带仅含有唯一的特征频率;然后,用所提方法和现有VMD(Variational Mode Decomposition)进行对比,验证了所提方法的可行性;最后将所提方法应用于齿轮箱复合故障振动信号中,成功提取出齿轮剥落和轴承外圈故障。所提方法为齿轮箱复合故障特征提取提供了一种新的思路。 相似文献
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为实现对滚动轴承振动信号中特征频率成分的精确提取,提出了将互补总体平均经验模态分解(complementary ensemble empirical mode decomposition,简称CEEMD)与小波包变换(wavelet package transform,简称WPT)相结合即CEMMD-WPT特征信号提取算法。两种方法的结合既有效解决了CEEMD分解后依然存在的模态混叠问题,又消除了进行WPT处理后产生虚假频率分量、频率混淆现象的影响。通过仿真试验验证了该方法的有效性,并应用于实际,取得很好的结果。 相似文献
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通过联合应用独立分量分析与小波包分解,提出一种混叠声源信号波形恢复的新方法。首先,借助基于奇异值分解的聚类分析估计原始多通道混合声观测中的独立源数。在此基础上,依据最小相关准则削减原始观测维数。随后,利用独立分量分析的冗余取消特性,由被削减的新观测出发初步抽取各独立源分量。最后,基于小波包分解进行信号消噪,实现多个混叠声源信号的波形恢复。实验结果表明,基于独立分量分析与小波包分解联合的新方法,能有效分离并正确恢复被噪声强烈污染的混叠声源信号波形,从而为后续的应用(如声源识别、声学故障诊断等)奠定了基础。 相似文献
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频率混叠在时域和频域现象中的研究 总被引:5,自引:1,他引:5
频率混叠(混淆)是数字信号处理中的特有现象,产生频率混叠后,信号处理会得出错误的分析结果。由于从模拟信号测试分析转入数字信号测试分析领域,很容易模糊频率混叠的概念,忽视频率混叠的现象。指出了频率混叠的危害及其在频域与时域中的现象,提出了混叠频率和混叠次数的计算公式,对避免和减少混叠现象的若干方法进行了讨论,并通过对混叠产生机理的研究,得出仅用数字滤波不能替代硬件抗混叠滤波的结论。 相似文献
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针对双树复小波变换存在频率混叠以及参数需自定义的缺陷,提出自适应改进双树复小波变换的齿轮箱故障诊断方法。首先,利用双树复小波变换将信号进行分解和单支重构,采用粒子群算法将分解后分量峭度值作为适应度函数,选择双树复小波的最优分解层数;其次,对重构出的低频信号进行频谱分析提取故障特征,将单支重构后的各高频分量进行变分模态分解,通过峭度值获得各高频分量经变分模态分解后的主频率分量信号;最后,分析各主频率分量信号的频谱,识别齿轮箱的故障特征。结果表明,该方法与双树复小波变换和变分模态分解相比,不仅消除了频率混叠现象,提高了信噪比和频带选择的正确性,而且还提高了从强噪声环境中提取瞬态冲击特征的能力。 相似文献
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Wen Bao Rui Zhou Jianguo Yang Daren Yu Ning Li 《Mechanical Systems and Signal Processing》2009,23(5):1458-1473
A troublesome problem in application of wavelet transform for mechanical vibration fault feature extraction is frequency aliasing. In this paper, an anti-aliasing lifting scheme is proposed to solve this problem. With this method, the input signal is firstly transformed by a redundant lifting scheme to avoid the aliasing caused by split and merge operations. Then the resultant coefficients and their single subband reconstructed signals are further processed to remove the aliasing caused by the unideal frequency property of lifting filters based on the fast Fourier transform (FFT) technique. Because the aliasing in each subband signal is eliminated, the ratio of signal to noise (SNR) is improved. The anti-aliasing lifting scheme is applied to analyze a practical vibration signal measured from a faulty ball bearing and testing results confirm that the proposed method is effective for extracting weak fault feature from a complex background. The proposed method is also applied to the fault diagnosis of valve trains in different working conditions on a gasoline engine. The experimental results show that using the features extracted from the anti-aliasing lifting scheme for classification can obtain a higher accuracy than using those extracted from the lifting scheme and the redundant lifting scheme. 相似文献
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The current morphological wavelet technologies utilize a fixed filter or a linear decomposition algorithm, which cannot cope with the sudden changes, such as impulses or edges in a signal effectively. This paper presents a novel signal processing scheme, adaptive morphological update lifting wavelet (AMULW), for rolling element bearing fault detection. In contrast with the widely used morphological wavelet, the filters in AMULW are no longer fixed. Instead, the AMULW adaptively uses a morphological dilation-erosion filter or an average filter as the update lifting filter to modify the approximation signal. Moreover, the nonlinear morphological filter is utilized to substitute the traditional linear filter in AMULW. The effectiveness of the proposed AMULW is evaluated using a simulated vibration signal and experimental vibration signals collected from a bearing test rig. Results show that the proposed method has a superior performance in extracting fault features of defective rolling element bearings. 相似文献
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小波包熵在设备性能退化评估中的应用 总被引:1,自引:0,他引:1
开展设备性能退化评估研究,是制定主动设备维护策略、降低设备维护费用的基础。在设备性能退化过程中,信号成份会逐渐复杂化。本文提出利用小波包熵监测信号的复杂性变化,从而为设备性能退化评估提供可靠的特征向量。为了研究性能退化过程中振动信号的小波包熵的变化规律,使用裂纹转子动力学模型模拟了转子裂纹逐渐增加的过程,并使用仿真数据计算了各个状态下的小波包能量熵和小波包奇异值熵值。分析结果表明,随着转子性能退化程度的加深,小波包熵值逐渐增加,且对于性能恶化的突变较为敏感。 相似文献
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Jiang Hong-kai Wang Zhong-sheng He Zheng-jia 《Frontiers of Mechanical Engineering in China》2006,1(2):199-203
Weak fault features of mechanical signals are usually immersed in noisy signals. A new wavelet method based on lifting scheme
to match weak fault characteristics is proposed. In this method, an initial set of finite biorthogonal filters is modified
by a lifting and dual lifting procedure alternately, and different lifting operators and dual lifting operators are obtained.
The properties of the initial wavelet is improved, and the new wavelet with particular properties is designed. Simulation
and engineering results confirm that the proposed method is better than other wavelet methods for extracting weak fault feature.
Modulus maxima of the detail signal in every operation cycle are extracted, the position and time that weak signal singularity
occurs are clearly found, and slight rub-impact fault caused by axis misalignment and rotor imbalance of a heavy oil catalytic
cracking set are desirably extracted. extracted.
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Translated from Journal of Xi’an Jiaotong University, 2005, 39(5) (in Chinese) 相似文献
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为解决工程实际中强噪声、非线性且频率成分复杂的振动信号降噪问题,提出了基于小波包分解和主流形识别的非线性降噪方法。采用小波包分解将原始振动信号正交无遗漏地分解到各频带范围内,根据各子频带中信噪空间分布,分别采用相应参数对小波包分解系数进行相空间重构;采用局部切空间排列(local tangent space alignment,LTSA)主流形识别方法在高维相空间中实现信号与噪音的分离,并重构出降噪后的一维小波包分解系数,最后进行小波包分解重构得到降噪后的振动信号。通过仿真实验和实例应用对本文所提方法的有效性进行了验证,试验结果表明本文方法具有良好的非线性降噪能力。 相似文献
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Stochastic noise in a fiber optic gyro (FOG) is mainly caused by white noise and 1/fγ fractal noise. The latter noise is characterized by long-term correlation, self-similarity and spectral density with 1/fγ power law. The application of the empirical mode decomposition (EMD) method and the lifting wavelet transform (LWT) as a novel EMD–LWT technique has been proposed and implemented in denoising the stochastic noise generated for a FOG. The EMD method is a novel nonlinear and non-stationary signal processing method and the LWT is a lifting scheme of wavelet transform. Experimental results of the FOG data have validated the feasibility of the proposed method, which is more effective than the denoising methods that use either LWT or the EMD method. 相似文献