Performance of Continuous Wavelet Transform over Fourier Transform in Features Resolutions

This study presents a comparative analysis of two image enhancement techniques, Continuous Wavelet Transform (CWT) and Fast Fourier Transform (FFT), in the context of improving the clarity of high-quality 3D seismic data obtained from the Tano Basin in West Africa, Ghana. The research focuses on a comparative analysis of image clarity in seismic attribute analysis to facilitate the identification of reservoir features within the subsurface structures. The findings of the study indicate that CWT has a significant advantage over FFT in terms of image quality and identifying subsurface structures. The results demonstrate the superior performance of CWT in providing a better representation, making it more effective for seismic attribute analysis. The study highlights the importance of choosing the appropriate image enhancement technique based on the specific application needs and the broader context of the study. While CWT provides high-quality images and superior performance in identifying subsurface structures, the selection between these methods should be made judiciously, taking into account the objectives of the study and the characteristics of the signals being analyzed. The research provides valuable insights into the decision-making process for selecting image enhancement techniques in seismic data analysis, helping researchers and practitioners make informed choices that cater to the unique requirements of their studies. Ultimately, this study contributes to the advancement of the field of subsurface imaging and geological feature identification.

PARAMETERS OPTIMIZATION OF CONTINUOUS WAVELET TRANSFORM AND ITS APPLICATION IN ACOUSTIC EMISSION SIGNAL ANALYSIS OF ROLLING BEARING

Morlet wavelet is suitable to extract the impulse components of mechanical fault signals. And thus its continuous wavelet transform （CWT） has been successfully used in the field of fault diagnosis. The principle of scale selection in CWT is discussed. Based on genetic algorithm, an optimization strategy for the waveform parameters of the mother wavelet is proposed with wavelet entropy as the optimization target. Based on the optimized waveform parameters, the wavelet scalogram is used to analyze the simulated acoustic emission （AE） signal and real AE signal of rolling bearing. The results indicate that the proposed method is useful and efficient to improve the quality of CWT.

一种基于优化VMD-CWT-CNN的柱塞泵配流盘磨损状态识别方法

为解决一维振动信号难以充分挖掘表达状态特征信息以及柱塞泵配流盘磨损早期识别问题,基于卷积神经网络(Convolutional neural networks,CNN)优秀的图像处理能力,提出了一个优化VMD-CWT-CNN模型。首先,采用连续小波变换(Continuous wavelet transform,CWT)对信号进行预处理,得到信号的二维时频图,作为CNN模型的一路输入,将状态识别问题转化为CNN图像识别问题。其次,基于相关系数对变分模态分解(Variational mode decomposition,VMD)参数优化后,利用优化VMD对振动信号进行预处理,再以相关系数和峭度值最大为优选原则,甄选出三组蕴含故障特征的本征模态函数(Intrinsic mode function,IMF),将其重组为三通道一维信号,作为CNN模型的另一路输入。最后,在CNN模型中将两路信息汇聚并得到柱塞泵配流盘磨损状态识别分类结果。实验中,此方法分别采用优化VMD和CWT对振动信号预处理,再结合CNN对磨损状态进行分类。实验结果表明,该方法对于配流盘磨损的三种状态的识别效果显著优于单路输入的CNN模型以及典型的深度学习方法和机器学习分类器。因此,优化的VMD-CWT-CNN方法可以更准确地实现柱塞泵配流盘磨损状态识别。

COMPUTATION OF CONTINUOUS WAVELET TRANSFORM AT DYADIC SCALES BY SUBDIVISION SCHEME

A new algorithm to compute continuous wavelet transforms at dyadic scales is proposed here. Our approach has a similar implementation with the standard algorithme a trous and can coincide with it in the one dimensional lower order spline case.Our algorithm can have arbitrary order of approximation and is applicable to the multidimensional case.We present this algorithm in a general case with emphasis on splines anti quast in terpolations.Numerical examples are included to justify our theorerical discussion.

基于VMD-CWT和CNN数控机床刀具健康诊断方法的研究

在数控机床切削过程中,刀具的健康状况直接影响产品的加工质量。因此,为了对刀具的健康状态作出诊断评价,本文提出了一种基于变分模态分解(VMD)和连续小波变换(CWT)特征提取与卷积神经网络(CNN)的刀具健康诊断方法。该方法首先采集不同健康状态下的刀具在切削时的振动信号,然后经VMD分解成若干IMF分量,并求解每个IMF分量的相关系数,选取相关系数较大的分量进行信号重构;其次采用连续小波变换来构造重构信号的时频图;最后将得到的时频图输入构建的CNN模型中,通过多层卷积、池化处理得到信号特征与刀具健康状态之间的准确映射,进而实现刀具的健康诊断。经实验验证表明,本文所提方法的识别准确率达到98.9%,具有良好的状态识别能力和泛化性,可为刀具健康诊断方法提供一定的理论依据。

基于连续小波变换和模型无关元学习的压燃式活塞发动机气门故障诊断研究

针对压燃式活塞发动机缸盖表面振动信号样本少及传统故障诊断方法特征提取和选择困难的问题,提出了一种基于连续小波变换(continuous wavelet transform,CWT)和模型无关元学习(model agnostic meta learning,MAML)的压燃式活塞发动机气门间隙异常故障诊断方法。通过将CWT的特征提取能力和MAML的快速学习能力相结合搭建故障诊断模型。试验结果表明该方法能有效识别气门间隙故障,并且其准确率高于传统基于CWT和卷积神经网络(convolutional neural network,CNN)的故障诊断方法。通过跨域故障对比试验,研究了不同气门故障类型对模型诊断能力的影响,验证了该方法在解决小样本和跨域故障问题时具有更高的准确率和泛化能力。

基于预训练GoogleNet模型和迁移学习的齿轮箱故障检测方法

针对以往齿轮箱故障诊断中特征处理算法繁琐、人为因素影响较大等问题,提出了一种基于预训练GoogleNet模型和迁移学习(TL)的故障诊断方法。首先,利用连续小波变换(CWT)将离散时间序列转变为二维小波尺度图,构建了样本集;然后,对预训练模型进行了结构微调及参数微调使其符合任务需求,利用处理得到的训练样本对微调后的模型进行了微训练,使其达到理想精度,然后保存模型,再将其应用于故障分类任务;最后,为了对上述模型的可行性进行验证,利用昆明理工大学控制与优化重点实验室的平行齿轮箱数据以及东南大学的行星齿轮箱数据对微调模型进行了验证。研究结果表明:相比于传统卷积神经网络(CNN)以及未经预训练的GoogleNet模型,基于预训练GoogleNet模型和迁移学习的故障诊断方法在训练样本较少的情况下,其分类准确率均值仍然高达97.40%,且模型的收敛速度更快,对计算机算力的依赖程度更低。微调模型高层的方法能根据任务分类情况个性化设置模型输出,因此该模型能够适用于不同的场景。

基于CWT和DWT相结合的谐波检测

提出了一种基于连续小波变换(CWT)和离散小波变换(DWT)相结合的电力系统谐波检测方法。首先利用CWT系数的幅值来检测谐波频率,该过程不用事先根据谐波次数确定分解层数,而只是确定尺度范围及步长,即可得出各次谐波频率。然后根据确定的谐波成分利用DWT来检测谐波幅值,并通过Matlab软件进行了仿真分析。仿真结果表明该方法有效地解决了基于离散小波变换的谐波检测方法中谐波次数未知而无法确定分解层数的难题,并能精确可靠检测各次谐波频率和相应的幅值。因此,CWT和DWT相结合是一种有效的电力系统谐波检测方法。

一种基于DWT-CWT的CPFSK信号盲符号速率估计算法

提出一种综合利用离散小波变换和连续小波变换来估计CPFSK信号符号速率的新算法。对离散小波变换后的细节信号进行连续小波变换,能够有效地减小噪声的影响,从而实现低信噪比下的符号速率估计。实验结果表明,与单纯使用连续小波变换的方法相比,新算法在低信噪比时具有较好的估计性能。

超音速旋流分离器湿气出口段液膜测量

超音速旋流分离技术作为一种新型的多相流分离技术,在天然气脱水脱烃领域有较好的应用前景。超音速分离器内液膜特性与分离性能息息相关,本文以其湿气出口段液膜特征为研究对象,开发了一套FPC电导式液膜厚度测量系统,设计了标定装置获取传感器实际输出特性。搭建实验管段展开气液两相分离实验,研究了入口含液量与背压比对液膜特征的影响。实验显示液膜厚度低于500μm时传感器灵敏度较高,拟合曲线误差在±5%以内。时域上统计学分析表明液膜厚度呈双峰分布,基层厚度约为70μm。频域上小波包分解后第2、第3频段与其他频段能量分布变化趋势有明显差异,据此对原始信号依次进行重构、局部均值分解、求近似熵和聚类,结果表明含液量增大,液膜波动加剧,近似熵可聚类为三类,对应三种流形。采用互相关算法分析液膜扰动波速度,含液量增大其流向分量与周向分量均呈指数增长趋势,流向分量及其RMSE范围分别为84.7~339.0mm/s、1.15~4.51mm/s,周向分量及其RMSE范围分别为54.8~186.4mm/s、1.20~3.38mm/s。

基于ResNet多特征图融合的钻削表面粗糙度分类方法

传统五面复合数控(CNC)钻削表面粗糙度测量工作复杂,采用人工测量存在较大人为误差。传统多元回归、多项式拟合方法仅采用转速和进给速度参数,数据利用率低且噪声敏感性强;用传统机器学习方法无法有效提取信号的深层复杂特征。针对上述问题,提出了一种基于ResNet模型、频谱图特征与时频图特征融合的钻削表面粗糙度分类预测方法。首先,根据CNC钻削加工理论和企业实际CNC钻削经验确定了CNC钻削加工实验的工艺参数变量;然后,基于SYNTEC CNC系统开发了多源数据采集系统,实时采集了钻削加工过程数据;接着,分析了三轴振动信号的频谱特征和时频特征,验证了振动信号跟表面粗糙度类别的关联性;随后,采用卡尔曼滤波对三轴振动信号进行了降噪处理,采用快速傅里叶变换(FFT)和连续小波变换(CWT)进行了振动信号频谱热图与时频图转换,采用矩阵拼接对三轴振动信号的单轴时频图进行了拼接融合,得到了三轴振动时频图;最后,对频谱热图和时频图进行了卷积运算融合频谱特征与时频特征,并进行了ResNet和其他网络模型如Densenet、Shufflenet和Mobilenet_v3_small等的对比实验。研究结果表明:相对上述其他网络模型,基于ResNet网络模型的表面粗糙度分类正确率提高了约9%,同时也验证了三轴时频特征融合以及频谱特征和时频特征融合方法的正确性。由于模型训练成本低、训练收敛速度快,该方法在轻量级、低成本的CNC机床钻削表面粗糙度预测分类中具有良好的工业应用前景。

基于改进MobileNetV2的轻量化轴承故障诊断

针对现有深度学习故障诊断模型普遍计算成本、时间成本和通信成本高,轻量化模型诊断精度低、抗噪能力弱,不适宜边缘环境部署的问题,提出了一种基于连续小波变换和改进MobileNetV2的轻量化轴承故障诊断方法。通过连续小波变换将原始信号转变为时频图作为输入样本,结合高效通道注意力机制与MobileNetV2,在较低参数量与计算量的情况下,提升了模型的故障诊断精度与抗噪能力。基于凯斯西储大学轴承数据集的实验表明,该方法在2 dB信噪比下准确率可以达到98.27%,与其他模型相比,在轻量化的同时具有更好的故障诊断精度和抗噪性,更适用于边缘场景下的故障诊断。

ConvNeXt网络及Stacked BiLSTM-Self-Attention在轴承剩余寿命预测中的应用

在滚动轴承剩余使用寿命预测方面,采用传统方法时存在鲁棒性差、精度低等各种问题。近些年来深度学习的发展为解决这些问题提供了新的思路。为了进一步提高对轴承寿命的预测精度,提出了一种基于ConvNeXt网络、堆叠双向长短时记忆网络(SBiLSTM)和自注意力机制(Self-Attention)的滚动轴承寿命预测方法。首先,采用连续小波变换(CWT)构造了振动信号的时频图,以更好地捕捉信号的时域和频域特征;然后,将得到的时频图输入到构建的ConvNeXt网络中,通过卷积、池化和层归一化等操作,对时频图的关键特征进行了提取;最后,将提取后的特征输入到SBiLSTM-Self-Attention模块中,进一步提取了时序信息和特征权重分配数据,利用PHM2012挑战数据集进行了验证,通过实验分析了该方法的均方根误差(RMSE)和平均绝对误差(MAE)。研究结果表明:相较于现有技术方法,该方法的平均RMSE为0.031;与其他三种方法,即卷积神经网络(CNN)、深度残差双向门控循环单元(DRN-BiGRU)和深度卷积自注意力双向门控循环单元(DCNN-Self-Attention-BiGRU)相比,其平均RMSE值分别下降了79%、74%和55%,MAE值分别下降了78%、73%和53%,说明该方法在滚动轴承剩余寿命预测中有较好的性能。

An ensemble learning method to retrieve sea ice roughness from Sentinel-1 SAR images

Sea ice surface roughness(SIR)affects the energy transfer between the atmosphere and the ocean,and it is also an important indicator for sea ice characteristics.To obtain a small-scale SIR with high spatial resolution,a novel method is proposed to retrieve SIR from Sentinel-1 synthetic aperture radar(SAR)images,utilizing an ensemble learning method.Firstly,the two-dimensional continuous wavelet transform is applied to obtain the spatial information of sea ice,including the scale and direction of ice patterns.Secondly,a model is developed using the Adaboost Regression model to establish a relationship among SIR,radar backscatter and the spatial information of sea ice.The proposed method is validated by using the SIR retrieved from SAR images and comparing it to the measurements obtained by the Airborne Topographic Mapper(ATM)in the summer Beaufort Sea.The determination of coefficient,mean absolute error,root-mean-square error and mean absolute percentage error of the testing data are 0.91,1.71 cm,2.82 cm,and 36.37%,respectively,which are reasonable.Moreover,K-fold cross-validation and learning curves are analyzed,which also demonstrate the method’s applicability in retrieving SIR from SAR images.

基于改进阈值DT-CWT降噪与WPHM模型的滚动轴承可靠度评估

针对滚动轴承运行数据集降噪难度大,且其可靠度评估模型预测精度不高的问题,提出了一种基于改进阈值DT-CWT降噪与WPHM模型的滚动轴承可靠度评估方法。首先,在数据降噪方面,利用一种改进的双树离散连续小波变换(DT-CWT)阈值降噪方法,将尺度因子和平移因子离散化,通过两组平行且独立的低通和高通滤波器,构成实部树和虚部树,实现了对信号的完全重构;然后,在数据处理方面,采用了PRONOSTIA实验台的全寿命实验数据,把粒子群优化(PSO)的全局最优搜索策略与最小二乘(LS)进行了融合,得到了威布尔比例风险模型(WPHM)的最佳参数;最后,采用法国弗朗什孔泰大学FEMTO的PRONOSTIA轴承实验台数据,对改进DT-CWT阈值降噪效果进行了评估,并对通过WPHM模型计算得到的可靠度评估曲线进行了验证。研究结果表明:(1)相对于传统阈值函数,改进阈值DT-CWT函数降噪效果更好,可提高信噪比(SNR)58.2%,降低均方根差(RMSE)58.3%,降噪信号变化趋势与原信号保持一致;(2)利用全寿命实验数据和PSO-LS方法对β、η和γ进行了参数估计,解决了WPHM模型中的参数估计问题;(3)可靠度评估曲线与轴承实际退化状态相符,WPHM模型可以反映轴承的健康状态。

动态杂散电流干扰下管地电位的波动特征

在地铁、高铁和高压直流(HVDC)输电系统接地极双极运行引起的动态杂散电流干扰下,现场监测得到196组管地电位数据。在时域上分析了不同干扰下管地电位的动态波动特征,然后利用信号学上连续小波变换(CWT)的方法在频域上研究了不同干扰下管地电位的动态波动特性,统计了不同干扰下管地电位的周期分布范围,并对比了其周期分布的差异。结果表明:三种干扰源都对管道造成动态直流干扰,其中地铁和高铁干扰呈现白天干扰严重、夜间平稳的周期性变化,而HVDC干扰则与高压直流输电系统接地极入地电流的大小与方向有关,没有明显的昼夜差别;高铁交流干扰同时伴随着显著的直流干扰;地铁直流干扰的周期占比随周期增大呈现先增大后减小的趋势,因高铁线路不同,管道干扰的周期分布也有所不同,但主要分布在大于500s的范围,HVDC干扰引起的管地直流电位波动周期分布较为均匀。

Recognition of Milankovitch cycles in the stratigraphic record: application of the CWT and the FFT to well-log data

The authors applied a the combination of Continuous Wavelet Transform (CWT) and Fast Fourier Transform (FFT) methods to gamma ray well-log data from the Q3, G1 and D2 wells. This high-resolution stratigraphic study was based on Milankovitch's orbital cycle theory. It was found that the CWT scale factors, ‘a,’ of 12, 24 and 60 match the ratios of the periodicities of precession, obliquity and eccentricity very well. Nine intervals of the Permo-carboniferous strata were recognized to have Milankovitch cycles in them. For example, section A of well Q3 has 29 precession cycles, 15 obliquity cycles and 7 short eccentricity cycles. The wavelengths are 2.7, 4.4 and 7.8 m for precession, obliquity and eccentricity, respectively. Important geological parameters such as the stratigraphic completeness and the accumulation rate were also estimated. These results provide basic information for further cyclostratigraphic correlation studies in the area. They are of great significance for the study of ancient and future climate change.

基于CWT模极大均值法的主动声呐回波展宽提取

为解决主动声呐目标回波展宽在远场、低信噪比和单频发射信号条件下提取困难的问题,提出了一种基于连续小波变换(CWT)和多径效应的主动声呐回波展宽提取方法。首先,利用CWT模极大均值法提取回波时间宽度,并通过仿真试验验证了该方法对单亮点和多亮点模型的有效性;然后,考虑远场条件下多径效应的影响,通过初始掠射角和声线到达的深度关系,计算了本征声线并获得多径效应造成的回波展宽;最后,结合发射信号长度提取由目标尺寸引起的回波展宽。该方法具有稳定性高、对发射信号类型和信噪比要求低且易于实现等特点,对水下目标分类识别具有参考价值。

Oil Transmission Pipelines Condition Monitoring Using Wavelet Analysis and Ultrasonic Techniques

In this paper, a reliable and sensitive diagnostic method for oil pipelines based on wavelet analysis and ultrasonic technique. This will be achieved by the use of 3-D finite element modeling software (Abaqus CAE 6.10) combined with a power full wavelet based signal processing technique will be used to collect the empirical ultrasonic data to validate the developed diagnostic method. The affect known seeded faults i.e., 1 mmhole at 25%, 50%, 75% and 100% depth in pipe wall were investigated using FEM techniques. A developed acoustic transceiver (Air Ultrasonic Ceramic Transducer 235AC130) will be used to collect the empirical ultrasonic data to validate the developed diagnostic method. The amplitudes and frequency spectra of the ultrasonic signals were measured and the predicted results were found to be in good agreement with the measured data, and that to confirm that this method can provide important information on pipe defects.

Characteristics of Surface Water Quality Affected by Agricultural Non-point Sources in Guigang City

[Objectives]To analyze the influence characteristics of surface water quality by agricultural non-point sources in Guigang City of Guangxi.[Methods]The daily concentration series of water quality indicators at three state-controlled monitoring stations in Guigang City from^(2)019 to 2021 was analyzed by using Daubechies(db)wavelet,and Morlet wavelet was used to analyze the daily average concentration of water quality indicators.Continuous wavelet transform(CWT)was used to analyze the monthly concentration series of water quality indicators at three state-controlled monitoring stations in Guigang City from^(2)014 to 2021.[Results]The Daubechies(db)wavelet analysis showed that the concentrations of COD_(Mn),TP,and TN had the maximum values during June-July and October-November,and there were spatial differences among monitoring stations(COD_(Mn) concentration exceeding the standard was the most serious in Shizui,and DO concentration not up to standard was the most in Thermal Power Plant,and NH_(3)-N,TP and TN exceeding the standard was the most in Wulin Ferry).Morlet results showed that principal period of wavelet variance graphs of COD_(Mn),NH_(3)-N,and TP was 340 d,and there was the same sub-period of 140 d,and principal period of wavelet variance graph of DO was 260 d.CWT results showed that COD_(Cr) had similar resonance periods of about 1-2 and 5-7 months;BOD 5 and COD_(Mn) was dominant by the resonance period of 1-4 months(2014-2017);DO had a similar resonance period of about 1-3 months;NH_(3)-N was dominant by the resonance period of 1-5 months.[Conclusions]The surface water quality of Guigang City was mainly affected by the residual nitrogen and phosphorus nutrients and pesticide residues from agricultural production activities.