Study on multi-frequency weak signal detection method based on stochastic resonance tuning by multi-scale noise

In practical engineering applications, useful information is often submerged in strong noise and the feature information is difficult to be extracted. Aimed at the detection problem of multi-frequency signal under colored noise background, a novel weak signal detection method based on stochastic resonance (SR) tuning by multi-scale noise is proposed. Firstly, noisy signal is processed by orthogonal wavelet transform to decompose the signal into multi-scale ingredients. According to the orthogonal wavelet transform coefficients characteristics of 1/f distribution, multi-scale noise is constructed so as to make the frequency-band containing the driving frequency be enhanced through SR system. Thus multi-frequency weak signal is detected. The method is effective to detect multi-frequency weak signal under colored noise background. Experiment signal analysis results show that the proposed method is simple for multi-frequency weak signal detection, and has good prospects for engineering applications.     

基于小波变换技术的发动机异响故障诊断

针对发动机异响故障信号呈非平稳时变特征并伴随有强烈的背景噪声,提出一种基于小波细节系数自相关性分析的分层阈值降噪法,该方法对信号进行离散小波变换,将信号分解为近似系数和细节系数,求出各层细节系数的自相关序列,根据序列是否呈白噪声自相关特性确定该层阈值。信号经过分层阈值降噪后,再进行连续小波变换,画出时频图,结合时域特征和频域特征确定故障类别。试验研究首先以模拟的信号模型为例,再针对实际的活塞敲缸响和曲轴轴承响两种常见异响故障进行比较分析,结果表明,分层阈值降噪法可以提高信噪比,恢复较高频率的有用信号,小波时频图可以清晰地呈现故障信号的时域和频域特征,为诊断提供一种切实可行的策略。     

DT-CWT相关滤波在齿轮箱故障诊断中的应用

根据小波系数的相关分析理论,提出了基于双树复小波变换的小波相关滤波法。该方法根据相邻层小波系数的相关性,通过迭代过程自适应地进行滤波,能够在达到良好降噪效果的同时保留微弱故障特征信息。对降噪后的信号进行希尔伯特包络分析便可准确得到故障特征频率。试验信号分析与工程应用结果表明,该方法能够有效提取强背景噪声下的齿轮箱轴承早期故障特征信息。     

小波理论-近似熵在风机故障诊断中的应用

风机的振动信号是一种典型的非平稳时变信号,具有混沌特征。提出用小波理论的滤波算法对风机原始振动信号进行降噪处理,用近似熵来定量描述风机的工作状态,进而对风机进行故障诊断。通过对风机不同工作状态下振动信号的分析,结果表明,风机在不同工作状态下所对应的近似熵有明显的变化,小波理论近似熵的方法适用于风机故障的检测。     

Gear fault diagnosis using active noise cancellation and adaptive wavelet transform

Acoustic signal from a gear mesh with faulty gears is in general non-stationary and noisy in nature. Present work demonstrates improvement of Signal to Noise Ratio (SNR) by using an active noise cancellation (ANC) method for removing the noise. The active noise cancellation technique is designed with the help of a Finite Impulse Response (FIR) based Least Mean Square (LMS) adaptive filter. The acoustic signal from the healthy gear mesh has been used as the reference signal in the adaptive filter. Inadequacy of the continuous wavelet transform to provide good time–frequency information to identify and localize the defect has been removed by processing the denoised signal using an adaptive wavelet technique. The adaptive wavelet is designed from the signal pattern and used as mother wavelet in the continuous wavelet transform (CWT). The CWT coefficients so generated are compared with the standard wavelet based scalograms and are shown to be apposite in analyzing the acoustic signal. A synthetic signal is simulated to conceptualize and evaluate the effectiveness of the proposed method. Synthetic signal analysis also offers vital clues about the suitability of the ANC as a denoising tool, where the error signal is the denoised signal. The experimental validation of the proposed method is presented using a customized gear drive test setup by introducing gears with seeded defects in one or more of their teeth. Measurement of the angles between two or more damaged teeth with a high level of accuracy is shown to be possible using the proposed algorithm. Experiments reveal that acoustic signal analysis can be used as a suitable contactless alternative for precise gear defect identification and gear health monitoring.     

结点阈值小波包变换语音增强新算法

人耳频率分辨率是非线性的,而小波包算法有灵活的时频分析能力,可较好的模拟人耳基底膜的频率分析特性。本文提出了一种新的基于结点阈值的小波包变换语音增强算法。采用Bark尺度小波包对含噪语音进行分解,在语音信号的子带层次上进行阈值操作,并采用软阈值方法进行阈值处理。采用谱熵法估计结点噪声。实验表明,该算法在多种噪声,尤其是有色噪声和非平稳噪声条件下均有较好的语音增强效果。     

EMD- and LWT-based stochastic noise eliminating method for fiber optic gyro

Stochastic noise in a fiber optic gyro (FOG) is mainly caused by white noise and 1/f^γ fractal noise. The latter noise is characterized by long-term correlation, self-similarity and spectral density with 1/f^γ 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.     

基于混合时频分析方法的风电机组故障诊断

针对风电机组传动链系统振动信号非高斯、非平稳性的特点,提出了一种基于混合时频分析的风电机组故障诊断方法。该方法首先采用参数优化Morlet小波消噪方法对原始振动信号进行分析,滤除强大的背景噪声干扰;进而通过自项窗方法抑制时频面的干扰项,增强信号特征成分,提取故障特征以实现故障诊断。在Morlet小波参数优化过程中,采用交叉验证法优化波形参数及连续小波变换的尺度参数;在自项窗的设计过程中,采用基于平滑伪魏格纳分布的函数进行设计,并通过两次阈值处理以减少运算量、提高运算效率。通过对风电机组监测振动数据分析,证明了该方法可以有效地实现背景噪声的消除和故障诊断。     

基于小波相关滤波法的滚动轴承早期故障诊断方法研究

目前基于小波分析的滚动轴承故障诊断方法研究已经很多,但是这些方法对于强噪声背景下的早期故障微弱信号特征提取效果并不理想。为此,提出了适用于强噪声背景的小波相关滤波滚动轴承早期故障诊断方法。该方法将小波相关滤波降噪方法和Hilbert包络细化谱分析相结合:对被测信号进行小波相关滤波降噪处理,对降噪处理后的高频段尺度域的小波系数进行Hilbert包络细化谱分析。该方法在滚动轴承的早期故障诊断中的试验结果表明,该方法与直接小波系数包络谱诊断方法相比,较大地增强了对滚动轴承早期故障诊断的能力,在强噪声背景下有效地提取出滚动轴承的早期故障频率。     

LYAPUNOV TUNING OF THE LEAKY LMS ALGORITHM FOR SINGLE-SOURCE,SINGLE-POINT NOISE CANCELLATION

Least-mean square (LMS) algorithms, which are commonly used for adaptive feedforward noise cancellation, have performance issues related to insufficient excitation, non-stationary reference inputs, finite-precision arithmetic, quantisation noise and measurement noise. Such factors cause weight drift and potential instability in the conventional LMS algorithm. Here, we analyse the stability and performance of the leaky LMS algorithm, which is widely used to correct weight drift. A Lyapunov tuning method is developed to find an adaptive leakage parameter and adaptive step size that provide optimum performance and retain stability in the presence of measurement noise on the reference input of known variance. The method accounts for non-persistent excitation conditions and non-stationary reference inputs and requires no a priori knowledge of the reference input signal characteristics other than a lower bound on its magnitude or a minimum signal-to-noise ratio. The Lyapunov tuning method is demonstrated for three candidate adaptive leakage and step size parameter combinations, each of which is a function of the instantaneous measured reference input, measurement noise variance, and/or filter length. These candidates illustrate stability vs performance tradeoffs in the leaky LMS algorithm elicited through the Lyapunov tuning method. The performance of each candidate Lyapunov tuned algorithm is evaluated experimentally in a single source, single-point acoustic noise cancellation system.     

Multiwavelet denoising with improved neighboring coefficients for application on rolling bearing fault diagnosis

The characteristic signal of a rolling bearing with a defect acts as a series of periodic impulses. These features are usually immersed in heavy noise and then difficult to extract. It is feasible to make the features distinct through wavelet denoising. Scalar wavelet thresholding has been used to extract features. However, scalar wavelet might not extract the feature available due to its limitation in some important properties, and conventional term-by-term thresholding does not consider the effect of neighboring coefficients. Since multiwavelets have been formulated recently and they might offer good properties in signal processing, a novel denoising method — multiwavelet denoising with improved neighboring coefficients (neighboring coefficients dependent on level, DLNeighCoeff for short) — is proposed in this article. The method proposed is applied to a simulated signal and fault diagnosis of locomotive rolling bearings, obtaining performance superior to conventional methods.     

基于MAP估计的复小波域局部自适应绝缘子红外热像去噪方法

为了从强白噪声干扰的红外热像中提取真实的绝缘子盘面温度场信息,提出一种基于MAP估计的复小波域局部自适应去噪方法.首次证实了绝缘子红外热像双树复小波变换(DT-CWT)系数服从拉普拉斯分布,并对不同滤波器组采用各自最精细分解层子带系数估计噪声方差,利用待估计点圆形邻域系数估计信号方差,且随分辨率变化调整圆形邻域半径,使得MAP估计的无噪声系数更为准确,提高了去噪图像质量.实验结果表明,该方法比传统的Wiener滤波法、基于离散小波变换和DT-CWT的贝叶斯阈值去噪方法具有更高的信噪比,在有效去除图像噪声的同时,图像细节信息保留更完好.     

基于同步压缩小波变换的滚动轴承故障诊断

针对滚动轴承故障诊断中存在的非平稳故障信号的特征提取困难这一难题,提出利用同步压缩小波变换(SWT)对故障信号的监测数据进行处理的方法。首先对信号进行连续小波变换(CWT),其次对小波变换系数进行同步压缩变换(SST),然后对SST系数进行自适应阈值去噪,之后在有效信号数据的频率中心附近进行积分提取,最后用提取到的有效信号进行重构。对实测的滚动轴承故障信号进行处理验证,结果表明,SWT具有较高的信号提取精度以及降噪能力,同时具有较高的时频分辨率,能够将故障信号转换为高分辨率的时频谱,弥补了CWT在这方面的不足。     

基于正态反高斯模型的自适应小波消噪方法

提出一种基于正态反高斯分布模型局部逼近小波系数的降噪算法。该算法以db5小波作为振动信号的分解小波,对噪声信号进行分解。对于分解过程中包含大量噪声的小波系数,利用具有良好细节逼近性能的正态反高斯分布构造先验模型,在先验模型的基础上,运用贝叶斯最大后验概率估计从含噪的小波系数中估计出真实的小波系数。在后验估计的过程中,对于估计模型中的关键系数采用粒子群算法进行优化选取。利用估计的小波系数来重构信号,得到降噪后的信号。通过仿真实验和实际轴承的故障信号对该方法进行了验证,结果表明,该方法具有较好的降噪效果,可以有效的消除信号的噪声。     

基于小波包分解与主流形识别的非线性降噪

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。     

Enhancing Weak Defect Features Using Undecimated and Adaptive Wavelet Transform for Estimation of Roller Defect Size in a Bearing

Defects in bearings affect the vibration level, resulting in and increase in temperature and decomposition of lubricant. Estimation of roller defect size is a complex task because it revolves as well as rotates during the motion. Signals from a defective roller of a bearing are superimposed by the signal from races, cage, and background noise. In this communication, a signal processing scheme is proposed that makes the signal suitable for estimating the size of the defect in the rolling element of a tapered roller bearing. To achieve this, in the first stage of processing, shift-invariant soft thresholding is applied to denoise the signal. It suppresses the noise without affecting defect-related features. Further, in the second stage of processing, continuous wavelet transform (CWT) using adaptive wavelet is applied. The adaptive wavelet is designed from the impulse extracted from the signal using the least squares fitting method. It results in higher coefficients in the region of impulse produced due to the defect. Finally, time marginal integration (TMI) of CWT coefficients is carried out for estimation of defect width. A study was performed for six different cases in which the size of the defect and orientation varies. Results of measurements of roller defect widths estimated using the proposed scheme were compared with defect widths calculated using image examination. For the nonoverlapping signature of defects (such as defects at 0° and 90° orientations), the maximum deviation in the width measurement using the proposed scheme is 6.52%. The error may increase when signature of two defects are overlapped.     

基于小波变换的信号去噪研究

介绍了小波变换理论,系统地研究了小波变换在信号处理尤其是信号滤波去噪方面的应用。根据不同类型的噪音．给出了基于不同小波变换的滤波算法并且对基于小波变换的滤波原理进行了分析。     

应用小波域三维Context模型的视频图像去噪

提出了一种基于三维小波变换和分块Context模型的视频去噪新方法（3DWTBCM）。视频图像序列的各帧之间具有较强的相关性,在三维小波变换域内去噪可以很好地将这种相关性加以利用。根据视频图像三维小波分解域内系数和噪声分布的特征,利用小波系数具有局部相关性对小波系数进行分块,将系数分解成各个局部区域。再将Context模型用于局部块中,按照能量分布将块内的小波系数分成多个子块。对各部分进行能量估计和多阈值估计,获得去噪声最佳阈值,有效地消除噪声。实验结果表明,3DWTBCM的噪声抑制效果明显优于各种2D去噪声方法,和常用的3D去噪声方法,PSNR平均提高1.5dB以上。从视觉效果来看,本文算法在去除噪声的同时,能较好的保留运动图像细节,运动物体显得比较平滑,不存在传统算法中的拖影、闪烁等现象。     

改进小波去噪Teager算子的齿轮微弱故障提取方法

针对齿轮箱在强噪声背景下齿轮微弱故障振动信号的特征不易被提取的问题,提出将改进小波去噪和Teager能量算子相结合的微弱故障特征提取方法。采用改进小波阈值函数对振动信号进行去噪处理,与形态学滤波和传统小波阈值函数相比能够有效地提高信号的信噪比。对去噪后的信号进行集合经验模态分解(ensemble empirical mode decomposition,简称EEMD)得到若干本征模式函数(intrinsic mode function,简称IMF),计算各IMF分量与原信号的相关系数并结合各IMF分量的频谱剔除虚假分量。对有效的IMF分量计算其Teager能量算子,并重构得到Teager能量谱,对重构信号进行时频分析并将其结果与原信号的希尔伯特黄变换(HilbertHuang transform,简称HHT)得到的边际谱进行对比。实验研究结果表明,本研究方法相比HHT能够对齿轮微弱故障特征进行更为有效地提取,验证了本研究方法在齿轮箱微弱故障诊断中的可行性。     

Rolling element bearing faults diagnosis based on optimal Morlet wavelet filter and autocorrelation enhancement

The fault diagnosis of rolling element bearing is important for improving mechanical system reliability and performance. When localized fault occurs in a bearing, the periodic impulsive feature of the vibration signal appears in time domain, and the corresponding bearing characteristic frequencies (BCFs) emerge in frequency domain. However, in the early stage of bearing failures, the BCFs contain very little energy and are often overwhelmed by noise and higher-level macro-structural vibrations, an effective signal processing method would be necessary to remove such corrupting noise and interference. In this paper, a new hybrid method based on optimal Morlet wavelet filter and autocorrelation enhancement is presented. First, to eliminate the frequency associated with interferential vibrations, the vibration signal is filtered with a band-pass filter determined by a Morlet wavelet whose parameters are optimized by genetic algorithm. Then, to further reduce the residual in-band noise and highlight the periodic impulsive feature, an autocorrelation enhancement algorithm is applied to the filtered signal. In the enhanced autocorrelation envelope power spectrum, only several single spectrum lines would be left, which is very simple for operator to identify the bearing fault type. Moreover, the proposed method can be conducted in an almost automatic way. The results obtained from simulated and practical experiments prove that the proposed method is very effective for bearing faults diagnosis.