Study on the Improvement of the Application of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise in Hydrology Based on RBFNN Data Extension Technology

The complex nonlinear and non-stationary features exhibited in hydrologic sequences make hydrological analysis and forecasting difficult.Currently,some hydrologists employ the complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)method,a new time-frequency analysis method based on the empirical mode decomposition(EMD)algorithm,to decompose non-stationary raw data in order to obtain relatively stationary components for further study.However,the endpoint effect in CEEMDAN is often neglected,which can lead to decomposition errors that reduce the accuracy of the research results.In this study,we processed an original runoff sequence using the radial basis function neural network(RBFNN)technique to obtain the extension sequence before utilizing CEEMDAN decomposition.Then,we compared the decomposition results of the original sequence,RBFNN extension sequence,and standard sequence to investigate the influence of the endpoint effect and RBFNN extension on the CEEMDAN method.The results indicated that the RBFNN extension technique effectively reduced the error of medium and low frequency components caused by the endpoint effect.At both ends of the components,the extension sequence more accurately reflected the true fluctuation characteristics and variation trends.These advances are of great significance to the subsequent study of hydrology.Therefore,the CEEMDAN method,combined with an appropriate extension of the original runoff series,can more precisely determine multi-time scale characteristics,and provide a credible basis for the analysis of hydrologic time series and hydrological forecasting.

A novel noise reduction technique for underwater acoustic signals based on complete ensemble empirical mode decomposition with adaptive noise,minimum mean square variance criterion and least mean square adaptive filter

Underwater acoustic signal processing is one of the research hotspots in underwater acoustics.Noise reduction of underwater acoustic signals is the key to underwater acoustic signal processing.Owing to the complexity of marine environment and the particularity of underwater acoustic channel,noise reduction of underwater acoustic signals has always been a difficult challenge in the field of underwater acoustic signal processing.In order to solve the dilemma,we proposed a novel noise reduction technique for underwater acoustic signals based on complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN),minimum mean square variance criterion(MMSVC) and least mean square adaptive filter(LMSAF).This noise reduction technique,named CEEMDAN-MMSVC-LMSAF,has three main advantages:(i) as an improved algorithm of empirical mode decomposition(EMD) and ensemble EMD(EEMD),CEEMDAN can better suppress mode mixing,and can avoid selecting the number of decomposition in variational mode decomposition(VMD);(ii) MMSVC can identify noisy intrinsic mode function(IMF),and can avoid selecting thresholds of different permutation entropies;(iii) for noise reduction of noisy IMFs,LMSAF overcomes the selection of deco mposition number and basis function for wavelet noise reduction.Firstly,CEEMDAN decomposes the original signal into IMFs,which can be divided into noisy IMFs and real IMFs.Then,MMSVC and LMSAF are used to detect identify noisy IMFs and remove noise components from noisy IMFs.Finally,both denoised noisy IMFs and real IMFs are reconstructed and the final denoised signal is obtained.Compared with other noise reduction techniques,the validity of CEEMDAN-MMSVC-LMSAF can be proved by the analysis of simulation signals and real underwater acoustic signals,which has the better noise reduction effect and has practical application value.CEEMDAN-MMSVC-LMSAF also provides a reliable basis for the detection,feature extraction,classification and recognition of underwater acoustic signals.

A Hybrid Air Quality Prediction Model Based on Empirical Mode Decomposition

Air pollution is a severe environmental problem in urban areas.Accurate air quality prediction can help governments and individuals make proper decisions to cope with potential air pollution.As a classic time series forecasting model,the AutoRegressive Integrated Moving Average(ARIMA)has been widely adopted in air quality prediction.However,because of the volatility of air quality and the lack of additional context information,i.e.,the spatial relationships among monitor stations,traditional ARIMA models suffer from unstable prediction performance.Though some deep networks can achieve higher accuracy,a mass of training data,heavy computing,and time cost are required.In this paper,we propose a hybrid model to simultaneously predict seven air pollution indicators from multiple monitoring stations.The proposed model consists of three components:(1)an extended ARIMA to predict matrix series of multiple air quality indicators from several adjacent monitoring stations;(2)the Empirical Mode Decomposition(EMD)to decompose the air quality time series data into multiple smooth sub-series;and(3)the truncated Singular Value Decomposition(SvD)to compress and denoise the expanded matrix.Experimental results on the public dataset show that our proposed model outperforms the state-of-art air quality forecasting models in both accuracy and time cost.

Missing interpolation model for wind power data based on the improved CEEMDAN method and generative adversarial interpolation network

Randomness and fluctuations in wind power output may cause changes in important parameters(e.g.,grid frequency and voltage),which in turn affect the stable operation of a power system.However,owing to external factors(such as weather),there are often various anomalies in wind power data,such as missing numerical values and unreasonable data.This significantly affects the accuracy of wind power generation predictions and operational decisions.Therefore,developing and applying reliable wind power interpolation methods is important for promoting the sustainable development of the wind power industry.In this study,the causes of abnormal data in wind power generation were first analyzed from a practical perspective.Second,an improved complete ensemble empirical mode decomposition with adaptive noise(ICEEMDAN)method with a generative adversarial interpolation network(GAIN)network was proposed to preprocess wind power generation and interpolate missing wind power generation sub-components.Finally,a complete wind power generation time series was reconstructed.Compared to traditional methods,the proposed ICEEMDAN-GAIN combination interpolation model has a higher interpolation accuracy and can effectively reduce the error impact caused by wind power generation sequence fluctuations.

Prediction of seawater pH by bidirectional gated recurrent neural network with attention under phase space reconstruction:case study of the coastal waters of Beihai,China

Marine life is very sensitive to changes in pH.Even slight changes can cause ecosystems to collapse.Therefore,understanding the future pH of seawater is of great significance for the protection of the marine environment.At present,the monitoring method of seawater pH has been matured.However,how to accurately predict future changes has been lacking effective solutions.Based on this,the model of bidirectional gated recurrent neural network with multi-headed self-attention based on improved complete ensemble empirical mode decomposition with adaptive noise combined with phase space reconstruction(ICPBGA)is proposed to achieve seawater pH prediction.To verify the validity of this model,pH data of two monitoring sites in the coastal sea area of Beihai,China are selected to verify the effect.At the same time,the ICPBGA model is compared with other excellent models for predicting chaotic time series,and root mean square error(RMSE),mean absolute error(MAE),mean absolute percentage error(MAPE),and coefficient of determination(R2)are used as performance evaluation indicators.The R2 of the ICPBGA model at Sites 1 and 2 are above 0.9,and the prediction errors are also the smallest.The results show that the ICPBGA model has a wide range of applicability and the most satisfactory prediction effect.The prediction method in this paper can be further expanded and used to predict other marine environmental indicators.

基于放电电压平台研究的蓄电池寿命状态评估

为解决传统动车组镍镉蓄电池的返修方法导致部分蓄电池在触发返修条件前已性能劣化,同时大量已达返修标准的蓄电池性能并未过度衰退的问题,设计单体镍镉蓄电池全寿命加速老化实验并获取相关实验数据。首先,采用集成经验模态方法建立单体电池全寿命健康状态类别划分模型,然后运用离散小波变换消除放电电压平台数据的奇异值,进而利用极限学习机算法预测蓄电池寿命状态,最终实现对蓄电池全生命周期寿命的准确预测与健康状态评估功能。实验结果表明:相较于传统的蓄电池寿命阈值分类方法,运用集成经验模态建立的健康状态类别划分模型能有效避免蓄电池寿命末端出现误警情况。作为融合算法模型输入的放电电压平台数据易获取,基于离散小波变换的数据预处理方法可提升算法准确率近3%,最终可达到96%~98%。此外,相对于传统的神经网络模型,融合算法模型不涉及迭代,因而能兼顾算法的预测精度与计算效能。蓄电池识别健康状态的F1值为0.976 3,识别老化阶段的F1值为0.950 9,识别故障阶段的F1值为0.939 394。相较于传统的依据动车组运营里程和使用年限进而决定蓄电池是否返修的方法,融合算法模型提供了显著的评判标准,能判别蓄电池是否应该返修,并有效地识别蓄电池的健康状态,降低了动车组的运营成本,保障动车组运营安全,为电池寿命评判和检修策略的优化提供参考。

一种灰色关联分析优化ICEEMDAN的VP倾斜仪信号降噪模型

VP倾斜仪固体潮信号受仪器监测复杂环境限制,多含有大量环境噪声。为获得真实固体潮曲线,提出一种基于灰色关联分析优化改进的自适应噪声完备集合经验模态分解(ICEEMDAN)VP倾斜仪信号降噪模型(GRA-ICEEMDAN)。该方法首先将含噪信号进行ICCEMDAN处理,得到若干个固有模态函数(IMF),并依次排列与标记;然后基于这些IMF分别计算相关系数、互信息、R^(2)、Adj-R^(2)、MSE、SSE、RMSE、MAE、MAPE、样本熵等10个评价指标值,构建IMF可信度评价指标矩阵;最后借助灰色关联分析(GRA)计算各评价指标与不同IMF之间的关联系数和关联度,依据关联度大小对各个IMF进行排序,将排名靠前的IMF进行线性重构,即可完成信号降噪。仿真去噪实验和实测去噪实验均表明,GRA-ICEEMDAN模型优于卡尔曼滤波、70阶低通FIR滤波、Savitzky-Golay等经典降噪模型,能显著区分噪声成分和有效成分,原始信号分解后的重构误差与信号损失极小,可推广至其他仪器的复杂信号降噪中。

基于密度聚类模态分解的卷积神经网络和长短期记忆网络短期风电功率预测

近年来,随着碳达峰和碳中和“双碳”战略目标的提出,风力发电已成为可再生能源发电的关键部分。为提高风电功率短期预测的准确度,提出基于密度聚类与自适应噪声完备集成经验模态分解(complete ensemble empirical mode decomposition with adaptive noise,CEEMDAN)和卷积神经网络与长短期记忆网络结合的短期风电功率预测方法。首先,利用密度聚类将风电功率与天气特征分成不同类别的数据集,通过自适应噪声完备集成经验模态分解算法将不同类别的数据进行频域分解得到子序列分量。以此为基础,将不同的子序列分量与天气特征进行特征选择,输入到卷积神经网络与长短期记忆网络的预测模型。最后,将不同的预测结果进行叠加得到最终的预测结果。整个预测过程通过聚类、分解和特征选择,有效提高了短期风电功率预测的准确度。

基于特征判定系数的电力变压器振动信号故障诊断

变压器带电故障诊断对于保证电力变压器安全平稳运行具有重要的意义。针对变压器工作环境复杂且单一参数表征变压器故障类型不全面的问题,文中提出一种基于自适应噪声完备集合经验模态分解(complete ensemble empirical mode decomposition with adaptive noise,CEEMDAN)和特征熵权法(entropy weight method,EWM)进行故障诊断的方法。通过相关系数与峭度加权(correlation coefficient and weighted kurtosis,CCWK)原则筛选CEEMDAN分量并重构信号,在实现剔除冗余分量的同时,提升变压器振动信号特征的表征能力;利用EWM构建特征判定系数实现单一数据诊断变压器故障类型;通过主成分分析法减小混合域特征尺度,采用鸡群优化算法优化支持向量机(support vector machine,SVM)模型进行故障诊断。对某变电站110 kV三相油浸式变压器进行分析,结果表明与概率神经网络和SVM等变压器故障诊断方法相比,文中方法能在提前定性故障类型的同时,进一步提高变压器故障诊断的准确率与效率。

基于小波包分解与CEEMDAN能量熵的水电机组振动信号特征提取

针对水电机组振动信号非平稳、非线性及噪声问题,提出一种基于自适应噪声完备经验模态分解(CEEMDAN)与能量熵结合的特征提取方法,首先对采集的振动信号进行小波包降噪处理,然后对降噪后信号进行CEEMDAN分解,运用相关系数法筛选有效固有模态函数(IMF)并计算其能量熵,由此构建特征向量集,最后将其输入到海洋捕食者优化支持向量机算法(MPA-SVM)进行模式识别。基于模拟信号、实测信号验证所提特征提取方法的有效性,并与其他方法作对比。结果表明,基于小波包分解与CEEMDAN能量熵的特征提取方法能准确提取特征,有效区分机组不同状态,为工程领域提供了应用价值。

CEEMDAN-WPE-CLSA超短期风电功率预测方法研究

提出了一种结合自适应噪声完全集合经验模态分解、加权排列熵、卷积神经网络、长短期记忆网络和自注意力机制的超短期风电功率预测方法。首先,利用自适应噪声完全集合经验模态分解将原始风电功率时间序列自适应分解为一系列的模态分量,降低原始序列的非线性和波动性;其次,根据加权排列熵计算各模态分量间的相似性并对相似的分量进行重组,以修正自适应噪声完全集合经验模态分解的过度分解问题,使得修正后的模态分量更具规律性;最后,将重组后的分量输入卷积长短期记忆网络进行时序建模,并利用自注意力机制对卷积长短期记忆网络的神经元权重进行重新分配,提高了卷积长短期记忆网络对输入特征不确定性的适应能力。在此基础上,明确了自注意力机制和自适应噪声完全集合经验模态分解、加权排列熵在风电功率预测中的作用机制,以及风电功率信号包含的重要物理信息,证明了自适应噪声完全集合经验模态分解、加权排列熵以及自注意力机制在风电功率信号模态分解和长短期记忆网络隐层输出权重分配中的有效性。

CEEMDAN-SE-WT降噪方法在航空发动机燃油流量信号中的应用

燃油流量信号是反映发动机状态和计算飞机排放物排放量的重要信号,但飞机飞行过程中传感器采集信号时不可避免地会受到外界环境以及内部因素干扰。提出一种结合样本熵(sample entropy,SE)的完全自适应噪声集合经验模态分解(complete ensemble empirical mode decomposition with adaptive noise,CEEMDAN)与小波变换(wavelet transform,WT)的联合降噪方法。首先使用CEEMDAN对燃油流量信号进行分解得到本征模态分量,利用样本熵筛选含噪分量,并用相关系数与方差贡献率进行复核。对于含噪分量使用小波阈值降噪进行处理。最后将未处理的模态分量和完成降噪的模态分量重构得到最终燃油流量信号。通过与其他方法比较,CEEMDAN-SE-WT方法拥有最高信噪比为85.287,降噪后燃油消耗总量与飞机总重变化最为接近,可以认为该方法较大程度保留了燃油流量信号中的有效特征,为后续计算民机排放物排放总量提供了良好的数据支持。

基于自适应噪声完全集合经验模态分解与BiLSTM-Transformer的锂离子电池剩余使用寿命预测

锂离子电池剩余使用寿命(remaining useful life,RUL)是使用者十分关心的问题,其涉及电池的更换时间和安全。针对锂离子电池的电容量非线性变化趋势,提出了一种基于自适应噪声完全集合经验模态分解与双向长短期记忆网络-Transformer的锂离子电池剩余使用寿命预测方法。首先,利用自适应噪声完全集合经验模态分解方法对锂离子电池电容量数据进行分解。其次,使用串联的双向长短期记忆神经网络和Transformer网络对分解后得到的残差序列和本征模态分量序列进行建模预测。最后,将预测的若干本征模态分量序列和残差序列进行求和,并对求和之后的最终预测数据与原始数据进行RUL预测。采用NASA公开的电池数据集对所提方法进行验证,结果表明,所提方法的平均绝对误差、均方根误差、平均绝对百分比误差和绝对误差控制分别控制在0.0173、0.0231、1.2084%和3个循环周期以内,能够有效地提高锂离子电池RUL的预测精度。

基于强跟踪滤波器的水中高频振荡放电参数分析

为探明水中放电高频振荡阶段参数及其变化特性,提出一种基于自适应噪声完备集合经验模态分解(CEEMDAN)和强跟踪滤波器的时变参数辨识方法。通过该方法分解水中放电实验平台采集的电压、电流信号得到不同频率特征的信号分量,对最适应原始波形的信号分量开展Hilbert变换并求得相应的瞬时幅值、频率,进而得到所需的电阻和电感。实验数据离散度分析结果表明,放电进程中参数变化具有随机性,故利用强跟踪滤波器进一步对实验数据进行辨识处理,可有效地降低随机放电造成的离散性,并获得具备普适性的电阻值和电感值。偏离度分析结果表明,辨识电阻与测量数据除在气泡崩塌阶段随机性过大外,前期偏离度集中在23.26%以下,降低了偏离度处于80%~110%内数据点的干扰,电感偏离度集中在2.35%以下。该方法能够有效地应用于水中高频振荡放电过程的时变参数处理研究中。

基于ICEEMDAN和分布熵的SS-Y伸缩仪信号随机噪声压制方法

结合改进的自适应噪声完备集合经验模态分解(ICEEMDAN)与分布熵(DistEn),提出一种无需自定义算法参数、去噪效果较好的伸缩仪信号随机噪声压制方法。首先将伸缩仪信号进行ICEEMDAN处理,得到若干个本征模态函数(IMF);然后计算各IMF分量的分布熵值,根据不同分布熵值的大小和表征的分量信号混乱程度,有针对性地对各IMF进行取舍;最后进行线性重构。设计仿真信号去噪实验和SS-Y伸缩仪信号去噪实验,结果表明,基于ICEEMDAN-DistEn去噪模型的伸缩仪信号重构还原度较好,去噪效果显著,明显优于CEEMDAN-DistEn、小波去噪和卡尔曼滤波等去噪模型。

声波法在输油管道上的可检测泄漏率分析

为了确保输油管道出现泄漏时能够及时检测到泄漏,对声波法在输油管道上的可检测泄漏率进行了分析。首先,建立了声波产生和传播衰减模型,根据模型分析了声波在管道内的衰减性,得到管道两端声波衰减幅值的计算方法;其次,采用基于互信息优化的自适应噪声完备集合经验模态分解(complete ensemble empirical mode decomposition with adaptive noise,简称CEEMDAN)和互谱分析相结合的去噪算法对采集的信号进行去噪,并实验验证了衰减模型可准确估计声波的衰减幅值,去噪算法可以有效消除信号中的噪声;最后,分析了管道特性与声波法在输油管道上的可检测泄漏率之间的关系。研究表明,管道特性在一定程度上决定了声波法的可检测泄漏率,声波法在输油管道上的最小可检测泄漏率可达0.45%。

基于状态划分和集成学习的轴承剩余使用寿命预测模型

针对滚动轴承剩余使用寿命(RUL)预测退化起始时间(DST)难以确定,以及单一寿命预测模型精度比较低的问题,提出了一种基于状态划分和集成学习模型的滚动轴承RUL预测方法。首先,提取了轴承振动信号的特征,利用滑动窗口不断更新3σ准则预警范围,结合连续触发机制自适应确定DST;然后,采用具有自适应噪声的完全集成经验模态分解(CEEMDAN)对退化阶段信号序列进行了自适应分解;最后,构建了集成学习模型,考虑分量的不同特性进行了多步滚动预测,融合预测结果得到了轴承RUL,采用滚动轴承XJTU-SY公开数据集进行了试验验证。研究结果表明:与基于长短时记忆神经网络(LSTM)、反向传播神经网络(BPNN)的预测方法相比,该方法预测结果的平均绝对误差分别降低了11.7%以及5.6%,相对均方根误差分别降低了12.2%以及10.7%,验证了该方法在轴承RUL预测中的有效性和优越性。

基于CEEMDAN-SBiGRU-OMHA的短期电力负荷预测

为了提高短期电力负荷预测的精准度,充分挖掘电力负荷数据的复杂相关性,提出了一种优化多头注意力机制的CEEMDAN-SBiGRU组合预测模型,改进了特征提取和特征融合两个模块.首先,采用自适应噪声完全集成经验模态分解(complete ensemble empirical mode decomposition with adaptive noise,CEEMDAN)将电力负荷数据分解成多个内在模态函数(IMF)和一个残差信号(RES);同时引入降噪自编码器DAE挖掘数据中受气象因素、工作日类型和温度变化的潜在特征.其次,将提取到的复杂特征输入至堆叠双向门控循环单元(stacked bidirectional gated recurrent unit,SBiGRU)模块中继续学习,以获取隐藏状态.最后,将获取的隐藏状态输入至加入残差机制和层归一化优化的多头注意力(optimized multi-head attention,OMHA)机制模块,可以准确地给重要特征分配更高的权重,解决噪声干扰问题.实验结果表明,CEEMDAN-SBiGRU-OMHA组合模型具有更高的精确性.

类EMD算法在桥梁振动数据降噪中的对比分析

在桥梁结构健康监测过程中,桥梁的振动信号易受外部环境影响而产生噪声,从而导致桥梁振动数据失真。针对此问题有着多种降噪算法,本文尝试使用经验模态分解(EMD)、集合经验模态分解(EEMD)和自适应噪声完全集合经验模态分解(CEEMDAN)3种算法分别对模拟仿真信号和桥梁振动信号数据进行降噪分析,进而探究3种算法的降噪性能。首先,使用3种算法分别处理原始信号数据,分离出不同的本征模态分量(IMF),并计算出其分量的方差比和相关系数;然后,利用参数筛选出有效分量进而重构信号;最后,以信噪比(SNR)与均方根误差(RMSE)作为评价指标,分析其降噪性能。结果表明CEEMDAN方法在3种方法中的降噪效果最好,而EEMD方法效果次之,EMD方法最弱。

基于EEMD与极限提升树的传感器故障诊断方法

传感器在工业互联等关键领域扮演着重要角色,通过故障诊断方法保障其数据的可靠性至关重要。本文提出一种基于EEMD与极限提升树的传感器故障诊断方法,以提高对传感器故障的准确识别和及时处理能力。该方法通过采集电压电流传感器数据,以故障注入方式建立数据集,利用EEMD对数据进行降噪与特征提取,并通过极限提升树进行训练,从而建立故障诊断模型。通过划分的验证集对所构建的模型进行验证。试验表明,该方法的测试准确性指标显著优于其他方法,至少高于次优方法11.95%的F1得分,能够更好地捕捉传感器数据的故障特征,具有较高的诊断准确率和较强的鲁棒性,且消耗较低、实用价值强,能够有力保障工业互联、玻璃智能制造等领域的数据准确性和安全性。