共查询到18条相似文献,搜索用时 125 毫秒
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对偶树复小波阈值降噪法及在机械故障诊断中的应用 总被引:1,自引:0,他引:1
为有效提取强噪声背景下微弱故障信号,提出了一种基于对偶树复小波的阈值降噪方法及其小波滤波器的设计原则,将其应用于机械故障诊断,取得了较好效果.阐述了对偶树复小波变换滤波器的设计要求和对偶树复小波阈值降噪法的实施步骤.该法充分利用了对偶树复小波变换的平移不变性的优良特性,试验表明:此法可以获得比常规的离散小波降噪更高的信... 相似文献
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对偶树复小波流形域降噪方法及其在故障诊断中的应用 总被引:1,自引:0,他引:1
滚动轴承工作环境比较复杂,现场测得的振动信号往往含有大量噪声且滚动轴承早期故障特征比较微弱容易被噪声所淹没,如何有效降低滚动轴承故障信号中的噪声准确提取故障特征是一个难题。将流形理论与对偶树复小波(Dual-tree complex wavelet transform, DTCWT)方法结合,提出一种对偶树复小波流形域降噪方法。将轴承振动信号进行对偶树复小波分解构造高维信号空间,然后利用最大方差展开流形算法(Maximum variance unfolding, MVU)提取高维信号空间中的真实信号子空间,去除噪声子空间,充分利用了MVU的非线性特征提取能力以及DTCWT的完全重构特征和平移不变性。运用仿真数据和滚动轴承工程信号对降噪方法进行检验,结果表明DTCWT_MVU可以有效消除轴承信号中的噪声成分,保持信号特征波形,提高信噪比,具有较强的工程使用价值和通用性。 相似文献
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基于双树复小波变换的轴承故障诊断研究 总被引:1,自引:0,他引:1
提出了一种基于双树复小波变换解调技术的轴承故障诊断新方法。该方法利用双树复小波变换具有近似平移不变性、避免频率混叠和有效降噪的优点,首先对轴承故障振动信号进行双树复小波分解和重构,将振动信号分解成实部和虚部,然后计算振动信号的双树复小波幅值包络和包络谱。齿轮箱轴承故障振动实验信号的分析表明,该方法能在强噪声环境下准确提取轴承故障产生的周期性瞬态冲击信号,能有效消除频率混叠现象和强噪声的影响,能有效识别轴承内圈和外圈故障。 相似文献
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基于Morlet小波与最大似然估计方法的降噪技术 总被引:2,自引:1,他引:2
采用与冲击信号匹配的Morlet小波作为小波基对信号进行小波变换,利用冲击信号的概率密度特征,结合最大似然估计的阈值方法进行降噪,以提取周期性的冲击信号。通过对减速箱故障信号进行降噪,提取出周期性的故障特征信号,表明该方法可以有效地去除强噪声干扰,提取振动冲击信号 相似文献
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基于软阈值和小波模极大值重构的信号降噪 总被引:1,自引:0,他引:1
软阈值小波降噪是一种常用的非平稳信号特征提取方法.为了改进软阈值小波降噪法的性能,提出一种基于软阈值和二进小波变换模极大值的新小波降噪方法.首先,对信号进行二进小波变换,再对小波系数进行软阈值处理;然后,选择由信号产生的小波系数模极大值点;最后,用交替投影算法重建信号.理论分析表明,该方法能有效地降低软阈值小波降噪法的误差下界.仿真试验表明,该方法提高了降噪结果的信噪比,且较好地保留了信号中的奇异性.将该方法和二进小波变换软阈值降噪法结合起来,应用于滚动轴承故障振动信号降噪.结果表明,该方法能有效地提取到信号中的冲击特征. 相似文献
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基于高密度小波变换对原始信号尺度划分更加精细的优势,将高密度小波变换、软阈值降噪和频谱分析相结合,提出了基于高密度小波变换的航空发动机滚动轴承故障诊断方法。该方法通过设定分解层数对信号进行高密度小波变换,得到每一尺度上的低频、中频、高频分量;对各分量软阈值降噪处理后进行频谱分析,进而实现故障特征频率的识别。利用仿真信号验证了高密度小波变换的有效性,通过航空发动机滚动轴承内圈故障和滚子故障工况下的试验信号进一步验证了该方法提取故障特征的能力,与传统小波变换方法的对比证明了该方法在抑制噪声干扰和故障特征频率识别方面的优势。 相似文献
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针对RV减速器在进行摆动疲劳试验时采集到的振动信号存在振动源复杂,噪声影响强,非线性变换等特点,利用传统的傅里叶变换(fast fourier transform,FFT)分析时存在“频率模糊”现象,不能准确地提取故障磨损点。针对上述问题,提出了一种阶次跟踪分析结合改进小波阈值降噪方法对RV减速器在疲劳实验时采集到的振动信号进行故障特征提取。首先利用阶次跟踪方法对采集到的非平稳时域振动信号进行等角度域转化;再利用改进小波阈值降噪法对等角度域信号进行阈值降噪;对得到的降噪后的等角度域信号进行FFT变换,得到阶次图。对比传统的小波降噪分析结果,该方法可以有效地提取出RV减速器在摆动疲劳实验中内部零部件发生的故障信息,为变转速旋转机械的故障诊断提供了基础。 相似文献
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NEW METHOD OF EXTRACTING WEAK FAILURE INFORMATION IN GEARBOX BY COMPLEX WAVELET DENOISING 总被引:3,自引:0,他引:3
CHEN Zhixin XU Jinwu YANG Debin 《机械工程学报(英文版)》2008,21(4):87-91
Because the extract of the weak failure information is always the difficulty and focus of fault detection. Aiming for specific statistical properties of complex wavelet coefficients of gearbox vibration signals, a new signal-denoising method which uses local adaptive algorithm based on dual-tree complex wavelet transform (DT-CWT) is introduced to extract weak failure information in gear, especially to extract impulse components. By taking into account the non-Gaussian probability distribution and the statistical dependencies among wavelet coefficients of some signals, and by taking the advantage of near shift-invariance of DT-CWT, the higher signal-to-noise ratio (SNR) than common wavelet denoising methods can be obtained. Experiments of extracting periodic impulses in gearbox vibration signals indicate that the method can extract incipient fault feature and hidden information from heavy noise, and it has an excellent effect on identifying weak feature signals in gearbox vibration signals. 相似文献
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Rolling element bearing fault detection based on optimal antisymmetric real Laplace wavelet 总被引:1,自引:0,他引:1
The presence of periodical impulses in vibration signals usually indicates the occurrence of rolling element bearing faults. Unfortunately, detecting the impulses of incipient faults is a difficult job because they are rather weak and often interfered by heavy noise and higher-level macro-structural vibrations. Therefore, a proper signal processing method is necessary. We proposed a differential evolution (DE) optimization and antisymmetric real Laplace wavelet (ARLW) filter-based method to extract the impulsive features buried in noisy vibration signals. The wavelet used in paper is developed from the fault characteristic signal model based on the idea of sparse representation in time-frequency domain. We first filter the original vibration signal using DE-optimized ARLW filter to eliminate the interferential vibrations and suppress random noise, then, demodulate the filtered signal and calculate its envelope spectrum. The analysis results of the simulation signals and real fault bearing vibration signals showed that the proposed method can effectively extract weak fault features. 相似文献
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Application of an improved kurtogram method for fault diagnosis of rolling element bearings 总被引:4,自引:0,他引:4
Yaguo Lei Jing LinZhengjia He Yanyang Zi 《Mechanical Systems and Signal Processing》2011,25(5):1738-1749
Kurtogram, due to the superiority of detecting and characterizing transients in a signal, has been proved to be a very powerful and practical tool in machinery fault diagnosis. Kurtogram, based on the short time Fourier transform (STFT) or FIR filters, however, limits the accuracy improvement of kurtogram in extracting transient characteristics from a noisy signal and identifying machinery fault. Therefore, more precise filters need to be developed and incorporated into the kurtogram method to overcome its shortcomings and to further enhance its accuracy in discovering characteristics and detecting faults. The filter based on wavelet packet transform (WPT) can filter out noise and precisely match the fault characteristics of noisy signals. By introducing WPT into kurtogram, this paper proposes an improved kurtogram method adopting WPT as the filter of kurtogram to overcome the shortcomings of the original kurtogram. The vibration signals collected from rolling element bearings are used to demonstrate the improved performance of the proposed method compared with the original kurtogram. The results verify the effectiveness of the method in extracting fault characteristics and diagnosing faults of rolling element bearings. 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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针对旋转机械故障信号的振动特点,将小波包络解调与基于数据融合技术的全矢谱相结合,提出一种诊断旋转机械调制信号的分析方法。首先,对安装在转子同一截面不同方向上的传感器信息同步整周期采样,对来自不同方向的时域信号分别采用小波包进行分解并重构,以实现带通滤波的效果;然后,采用全矢谱技术对两组重构信号进行数据融合;最后,对合成后的信号做包络解调分析。通过仿真研究和工程实例分析可以得出,对来自同一截面、不同方向的时域信号分别作小波包络谱分析时,两者在能量分布和频谱结构上存在着较大差别,以致造成提取故障信息的不完整或造成误判、漏判。基于小波包的全信息解调分析方法通过对同源的双通道信号的有效融合,可全面地反映出信号中包含的不同调制信息。与基于全矢谱的传统包络解调分析进行对比分析,具有较好的分析结果和可信度。 相似文献
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分别采用短时傅里叶变换和小波变换对雨刮直流电机的轴承异响和蜗轮蜗杆异响故障的振动和噪声信号进行了分析,得出了这两类故障的时频特性,为特征参数提取和实现故障诊断提供了直接依据。通过对比,初步验证了短时傅里叶分析和小波分析的正确性与适用性,发现小波分析更具有优势。 相似文献