Enhanced Fourier Transform Using Wavelet Packet Decomposition

Many domains, including communication, signal processing, and image processing, use the Fourier Transform as a mathematical tool for signal analysis. Although it can analyze signals with steady and transitory properties, it has limits. The Wavelet Packet Decomposition (WPD) is a novel technique that we suggest in this study as a way to improve the Fourier Transform and get beyond these drawbacks. In this experiment, we specifically considered the utilization of Daubechies level 4 for the wavelet transformation. The choice of Daubechies level 4 was motivated by several reasons. Daubechies wavelets are known for their compact support, orthogonality, and good time-frequency localization. By choosing Daubechies level 4, we aimed to strike a balance between preserving important transient information and avoiding excessive noise or oversmoothing in the transformed signal. Then we compared the outcomes of our suggested approach to the conventional Fourier Transform using a non-stationary signal. The findings demonstrated that the suggested method offered a more accurate representation of non-stationary and transient signals in the frequency domain. Our method precisely showed a 12% reduction in MSE and a 3% rise in PSNR for the standard Fourier transform, as well as a 35% decrease in MSE and an 8% increase in PSNR for voice signals when compared to the traditional wavelet packet decomposition method.

数控机床电动主轴WPD-TSNE-SVM模型故障诊断

为了提高数控机床电动主轴故障诊断效率,设计了一种WPD-TSNE-SVM组合模型。利用小波包方法分解主轴振动信号,并完成样本集TSNE降维的过程,利用SVM完成重构特征的故障分类。构建数控机床主轴信号混合特征空间向量,并进行故障诊断分析。研究结果表明:TSNE方法训练样数据形成规律分布特点,采用非线性SVM多故障分类器实现小波包混合特征的故障准确分类。根据径向基核函数建立的非线性SVM诊断方法获得更高准确率。该方法诊断轴承运行故障,获得更高维护效率,确保数控机床主轴运行稳定性。

基于WPD-ARIMA-GARCH组合模型的酱卤肉制品安全风险区间预测

针对传统确定性预测不能提供不确定性信息的难题,本研究提出了一种点估计和区间估计组合预测模型,并将其创新性地应用在食品安全风险预警领域。在点估计部分,使用小波包分解(wavelet packet decomposition,WPD)对周风险等级序列分解后,应用差分自回归移动平均(autoregressive integrated moving average,ARIMA)模型进行预测;在区间估计部分,使用广义自回归条件异方差(generalized autoregressive conditional heteroskedast,GARCH)模型对残差进行预测。本实验将建立的WPD-ARIMA-GARCH组合模型运用于某地区酱卤肉制品的风险预测,结果表明2019年的3月底和7月底该地区的酱卤肉制品安全风险较高,与实际情况相符;同时,该模型在10个不同地区的酱卤肉制品风险预测中,均方误差、平均绝对误差和平均绝对百分比误差分别为1.626、0.806和20.824;其90%置信区间的预测区间平均宽度和覆盖宽度标准值均为0.024,可以覆盖所有真实值。该模型具有较高的预测精度和较低的误差,能对酱卤肉制品质量安全起到风险防控作用,可为日常食品安全监管提供相应的技术支持。

基于WPD-EMD-WPD的地下工程微震信号降噪方法研究

微震信号中的背景噪声会影响初至拾取、震源定位及参数反演,合理有效的降噪方法是微震监测技术成功应用于工程建设的重要基础。本文提出一种基于WPD-EMD-WPD的方法抑制噪声,并采用信偏比衡量降噪效果。该方法首先对含噪信号小波包预降噪,实现初次滤波;然后对预降噪后的信号进行经验模态分解(Empirical Mode Decomposition,EMD),自适应分解得到IMFS,通过相关系数法确定IMFS分解分量位置;最后,对分界分量之前的高频分量小波包降噪,再与低频分量重构。分别使用小波包、EMD、EMD-WPD、WPD-EMD-WPD 4种方法进行仿真实验,对含噪Ricker子波降噪处理,通过对比降噪前后的降噪效果衡量指标、频谱图、波形图对比发现,WPD-EMD-WPD降噪效果更优,且信偏比与其他降噪指标有良好对应性。将该方法应用于国内某大型水电工程地下洞室,结果表明,该方法能获得更低的信偏比,且能更好地反映初至时刻和岩石微破裂信息。

基于WPD的光缆中间接头局部光纤通信信号去噪技术研究

光缆运行工作过程中,由于光缆质地较脆,抗机械摩擦强度较差,光缆中间接头部分易产生局部的通信信号噪声干扰。为此,设计基于小波包分解(WPD)的光缆中间接头局部光纤通信信号去噪方法。从白噪声和窄带噪声2个方面,结合损伤区域提取损伤后的局部噪声干扰特性,设置WPD算法的去噪阈值。以分层去噪的方式得出局部光纤通信信号去噪结果。实验结果表明,在不同的损伤干扰强度条件下,所提方法得出光纤通信信号的信噪比较高,去噪效果好。

基于PCA-WPD优化的电流互感器故障检测方法研究

针对电磁式电流互感器测量误差的长期稳定性较差问题,提出主成分分析小波包分解的电流互感器故障测量误差自检测方法。利用小波包分解优化残差统计量,可以消除电流互感器设备中节点不平衡和随机误差对检测分析结果的影响。实验结果表明,随着测量误差的增加,测量数据的残差统计量逐渐增加。所提出的电流互感器故障检测方法能较好地满足0.2级精度的要求,且可以检测到电流互感器异常数据占90.97%,占总数的53.17%。该方法能够及时准确地实现电流互感器故障测量误差的自检测。

基于CEEMDAN-PE-WPD和多目标优化的超短期风电功率预测方法

为了提高风电功率预测的精度,提出了一种基于总体平均经验模态分解(complete ensemble empirical mode decomposition with adaptive noise,CEEMDAN)、排列熵(permutation entropy,PE)、小波包分解(wavelet packet decomposition,WPD)和多目标优化的超短期风电功率预测方法。首先,利用CEEMDAN、PE和WPD构成的信号处理方法降低原始风电信号的随机性和波动性;然后,将分解后的子分量输入到长短期记忆(long short-term memory,LSTM)神经网络,并且利用精英T分布麻雀优化算法(elite t-distribution sparrow optimization algorithm,ETSSA)优化LSTM的隐藏层单元数,提升LSTM网络的预测性能;最后,建立多目标优化损失函数,将准确率、稳定度和合格率3个优化目标同时加入到损失函数中,综合提升模型的预测性能。对内蒙古某地区风力发电场的实测数据进行实验分析结果表明,与其他经典预测方法相比,所提方法提升风电功率预测性能有显著效果,并且在不同季节风况下预测效果良好。

A novel internet traffic identification approach using wavelet packet decomposition and neural network

Internet traffic classification plays an important role in network management, and many approaches have been proposed to classify different kinds of internet traffics. A novel approach was proposed to classify network applications by optimized back-propagation (BP) neural network. Particle swarm optimization (PSO) algorithm was used to optimize the BP neural network. And in order to increase the identification performance, wavelet packet decomposition (WPD) was used to extract several hidden features from the time-frequency information of network traffic. The experimental results show that the average classification accuracy of various network applications can reach 97%. Moreover, this approach optimized by BP neural network takes 50% of the training time compared with the traditional neural network.

Separation of closely spaced modes by combining complex envelope displacement analysis with method of generating intrinsic mode functions through filtering algorithm based on wavelet packet decomposition

One of the important issues in the system identification and the spectrum analysis is the frequency resolution, i.e., the capability of distinguishing between two or more closely spaced frequency components. In the modal identification by the empirical mode decomposition （EMD） method, because of the separating capability of the method, it is still a challenge to consistently and reliably identify the parameters of structures of which modes are not well separated. A new method is introduced to generate the intrin- sic mode functions （IMFs） through the filtering algorithm based on the wavelet packet decomposition （GIFWPD）. In this paper, it is demonstrated that the CIFWPD method alone has a good capability of separating close modes, even under the severe condition beyond the critical frequency ratio limit which makes it impossible to separate two closely spaced harmonics by the EMD method. However, the GIFWPD-only based method is impelled to use a very fine sampling frequency with consequent prohibitive computational costs. Therefore, in order to decrease the computational load by reducing the amount of samples and improve the effectiveness of separation by increasing the frequency ratio, the present paper uses a combination of the complex envelope displacement analysis （CEDA） and the GIFWPD method. For the validation, two examples from the previous works are taken to show the results obtained by the GIFWPD-only based method and by combining the CEDA with the GIFWPD method.

Time Domain Signal Analysis Using Wavelet Packet Decomposition Approach

This paper explains a study conducted based on wavelet packet transform techniques. In this paper the key idea underlying the construction of wavelet packet analysis (WPA) with various wavelet basis sets is elaborated. Since wavelet packet decomposition can provide more precise frequency resolution than wavelet decomposition the implementation of one dimensional wavelet packet transform and their usefulness in time signal analysis and synthesis is illustrated. A mother or basis wavelet is first chosen for five wavelet filter families such as Haar, Daubechies (Db4), Coiflet, Symlet and dmey. The signal is then decomposed to a set of scaled and translated versions of the mother wavelet also known as time and frequency parameters. Analysis and synthesis of the time signal is performed around 8 seconds to 25 seconds. This was conducted to determine the effect of the choice of mother wavelet on the time signals. Results are also prepared for the comparison of the signal at each decomposition level. The physical changes that are occurred during each decomposition level can be observed from the results. The results show that wavelet filter with WPA are useful for analysis and synthesis purpose. In terms of signal quality and the time required for the analysis and synthesis, the Haar wavelet has been seen to be the best mother wavelet. This is taken from the analysis of the signal to noise ratio (SNR) value which is around 300 dB to 315 dB for the four decomposition levels.

Features of energy distribution for blast vibration signals based on wavelet packet decomposition

Blast vibration analysis constitutes the foundation for studying the control of blasting vibration damage and provides the precondition of controlling blasting vibration. Based on the characteristics of short-time nonstationary random signal, the laws of energy distribution are investigated for blasting vibration signals in different blasting conditions by means of the wavelet packet analysis technique. The characteristics of wavelet transform and wavelet packet analysis are introduced. Then, blasting vibration signals of different blasting conditions are analysed by the wavelet packet analysis technique using MATLAB; energy distribution for different frequency bands is obtained. It is concluded that the energy distribution of blasting vibration signals varies with maximum decking charge,millisecond delay time and distances between explosion and the measuring point. The results show that the wavelet packet analysis method is an effective means for studying blasting seismic effect in its entirety, especially for constituting velocity-frequency criteria.

基于ASWPD-BO-GRU的月径流量预测模型

为提高月径流量预测精度,并针对传统分解集成径流预测模型错误使用未来数据的问题,提出并建立了基于自适应小波包分解(ASWPD)和贝叶斯优化(BO)的门控循环单元(GRU)月径流量预测模型(ASWPD-BO-GRU)。首先,利用ASWPD对原始月径流量时间序列进行分解,在不使用未来数据的前提下得到4个相对规律的分解子序列,以降低预测难度;然后,利用BO优选分解后的子序列对应的GRU模型超参数;最终,对每个子序列进行预测,将预测结果相加重组得出月径流量预测结果。将提出并建立的模型应用于黑河流域莺落峡水文站月径流量预测中,并与GRU、BO-GRU、WPD-BO-GRU模型(基于传统分解思想对原始月径流量时间序列整体进行分解的预测模型)的预测结果进行对比。结果表明:ASWPD-BO-GRU模型的纳什效率系数(NSE)为0.89,在实例应用中预测精度最高,说明ASWPD-BO-GRU模型在正确分解的前提下具有较高的预测精度和更强的泛化能力。

Adaptive Bearing Fault Diagnosis based on Wavelet Packet Decomposition and LMD Permutation Entropy

Bearing fault signal is nonlinear and non-stationary, therefore proposed a fault feature extraction method based on wavelet packet decomposition （WPD） and local mean decomposition （LMD） permutation entropy, which is based on the support vector machine （SVM） as the feature vector pattern recognition device Firstly, the wavelet packet analysis method is used to denoise the original vibration signal, and the frequency band division and signal reconstruction are carried out according to the characteristic frequency. Then the decomposition of the reconstructed signal is decomposed into a number of product functions （PE） by the local mean decomposition （LMD） , and the permutation entropy of the PF component which contains the main fault information is calculated to realize the feature quantization of the PF component. Finally, the entropy feature vector input multi-classification SVM, which is used to determine the type of fault and fault degree of bearing The experimental results show that the recognition rate of rolling bearing fault diagnosis is 95%. Comparing with other methods, the present this method can effectively extract the features of bearing fault and has a higher recognition accuracy

Wavelet packet decomposition entropy threshold method for discrete spectrum interferences rejection of on-line partial discharge monitoring

The frequency domain division theory of dyadic wavelet decomposition and wavelet packet decomposition (WPD) with orthogonal wavelet base frame are presented. The WPD coefficients of signals are treated as the outputs of equivalent bandwidth filters with different center frequency. The corresponding WPD entropy values of coefficients increase sharply when the discrete spectrum interferences (DSIs), frequency spectrum of which is centered at several frequency points existing in some frequency region. Based on WPD, an entropy threshold method (ETM) is put forward, in which entropy is used to determine whether partial discharge (PD) signals are interfered by DSIs. Simulation and real data processing demonstrate that ETM works with good efficiency, without pre-knowing DSI information. ETM extracts the phase of PD pulses accurately and can calibrate the quantity of single type discharge.

WPD-COA-EL M模型在汛期月降水量时间序列预测中的应用研究

针对月降水量时间序列多尺度非平稳性特点,提出小波包分解(WPD)-白骨顶鸟优化算法(COA)-极限学习机(ELM)相融合的降水量预测模型。首先,利用WPD将非平稳月降水量时间序列分解为若干子序列分量;然后在不同维度条件下利用6个典型函数对COA进行仿真测试;利用COA优化ELM输入层权值和隐含层偏值,对每一个子序列分量分别建立COA-ELM模型进行预测,将预测结果叠加重构后即为最终预测结果;最后,以云南省龙潭站汛期和主汛期月降水量数据为例进行实验,并与WPD-COA-BP、WPD-ELM、WPD-BP预测模型进行比较。结果表明:COA在不同维度条件下均具有较好的寻优精度和全局搜索能力。WPD-COA-ELM模型对实例汛期、主汛期月降水量时间序列预测的平均绝对百分比误差分别为3.91%、3.59%,预测精度优于WPD-COA-BP模型,远优于WPD-ELM.WPD-BP模型。WPD能科学降低月降水时间序列数据的复杂性,提高预测效果;COA能有效优化ELM输入层权值和隐含层偏值,提高ELM网络性能。

基于WPD-AHA-ELM模型的水质时间序列多步预测

根据水质时间序列多尺度、非平稳特性,并基于“分解-预测-重构”思想,提出小波包分解(WPD)-人工蜂鸟算法(AHA)-极限学习机(ELM)组合多步预测模型,并应用于云南省昆明西苑隧道断面pH值、CODmn、DO、NH_(3)-N多步预测.首先介绍AHA原理,在不同维度条件下选取6个典型函数对AHA进行仿真测试,并与灰狼优化(GWO)算法、旗鱼优化(SFO)算法、粒子群优化(PSO)算法的仿真结果进行比较;其次利用WPD对水质时序数据进行小波包分解,以降低水质序列数据的复杂度;并在延迟时间为1的情况下,采用Cao方法确定各子序列分量的输入、输出;最后通过各分量训练样本构建ELM适应度函数,采用AHA对适应度函数进行寻优,利用寻优获得的最佳ELM输入层权值和隐含层偏值建立WPD-AHA-ELM模型对各子序列分量进行超前1步至超前5步预测,将预测结果加和重构得到最终多步预测结果.结果表明:AHA具有较好的寻优精度和全局搜索能力,寻优精度优于GWO、SFO、PSO算法.WPD-AHA-ELM模型对实例断面pH、CODmn、DO、NH_(3)-N超前1步至超前5步预测的平均绝对百分比误差分别在0.05%~1.23%、0.10%~3.15%、0.13%~3.67%、0.65%~10.6%之间,具有较小的预测误差,其中尤以超前1步至超前3步的预测效果最好.WPD-AHA-ELM模型预测精度随着超前预测步数的增加而降低.

基于WPD-tSNE-SVM方法的电站机组主轴故障诊断分析

为提高电站机组主轴故障诊断效率,设计一种WPD-tSNE-SVM组合模型,采用小波包混合特征与支持向量机(SVM)对电站机组轴承开展故障诊断。研究结果表明:采用t分布式邻域嵌入方法降维数据呈现规律分布特征,说明小波包混合特征提取方法能够满足有效性。非线性SVM多故障分类器能够满足小波包混合特征的精确故障分析,各分类器都可以实现小波包混合特征集的高效分类,以径向基核函数设置的非线性SVM诊断方式达到了更高的准确率,从而为之后的维护保养过程提供参考价值,促进维护效率的进一步提升,有效保障电站机组主轴处于稳定运行状态。根据该方法诊断主轴轴承运行故障,为后续维护保养提供指导意义,获得更高的维护效率,确保电站机组主轴运行稳定性。

基于物理模型驱动优化WPD的弧齿锥齿轮故障诊断方法研究

弧齿锥齿轮作为收获机主动力输出的关键零部件,其故障表现通常为激励脉冲,为实现农业收获机主传动齿轮箱故障及时有效地监测诊断,本文提出基于物理模型驱动优化的小波包分解方法(wavelet packet decomposition,WPD)。针对齿轮损伤的多分量调制现象,该方法根据小波基函数特定时频窗口分析信号的特点,通过建立齿轮损伤集中参数模型,辅助筛选适应齿轮损伤特性的小波包分解系数,以此优化分解信号所选用的小波基函数,使之具有更好的提取齿轮故障特征信息的能力。通过对实验信号和藠头收获机齿轮故障信号的包络谱分析,验证了该方法能够有效地应用于收获机齿轮故障诊断。

基于BA-MKELM的微电网故障识别与定位

提出一种基于贝叶斯算法优化多核极限学习机的微电网故障识别和定位方法。针对极限学习机输入参数和隐含层节点数随机选取导致回归能力不足的问题,引入核函数,将多项式与高斯径向基核函数加权组合构成多核极限学习机建立故障识别与定位模型,并采用贝叶斯算法对多核极限学习机相关参数进行优化,进一步提高模型的逼近能力。为了验证所提模型的故障识别与定位性能,选用极限学习机和多核极限学习机分别建立故障诊断模型进行比较分析。实验结果表明,所提方法能够高性能地识别和定位微电网中任何类型的故障,识别和定位精度更高。