Significant wave height forecasts integrating ensemble empirical mode decomposition with sequence-to-sequence model

As wave height is an important parameter in marine climate measurement,its accurate prediction is crucial in ocean engineering.It also plays an important role in marine disaster early warning and ship design,etc.However,challenges in the large demand for computing resources and the improvement of accuracy are currently encountered.To resolve the above mentioned problems,sequence-to-sequence deep learning model(Seq-to-Seq)is applied to intelligently explore the internal law between the continuous wave height data output by the model,so as to realize fast and accurate predictions on wave height data.Simultaneously,ensemble empirical mode decomposition(EEMD)is adopted to reduce the non-stationarity of wave height data and solve the problem of modal aliasing caused by empirical mode decomposition(EMD),and then improves the prediction accuracy.A significant wave height forecast method integrating EEMD with the Seq-to-Seq model(EEMD-Seq-to-Seq)is proposed in this paper,and the prediction models under different time spans are established.Compared with the long short-term memory model,the novel method demonstrates increased continuity for long-term prediction and reduces prediction errors.The experiments of wave height prediction on four buoys show that the EEMD-Seq-to-Seq algorithm effectively improves the prediction accuracy in short-term(3-h,6-h,12-h and 24-h forecast horizon)and long-term(48-h and 72-h forecast horizon)predictions.

Pressure fluctuation signal analysis of pump based on ensemble empirical mode decomposition method

Pressure fluctuations, which are inevitable in the operation of pumps, have a strong non-stationary characteristic and contain a great deal of important information representing the operation conditions. With an axial-flow pump as an example, a new method for time-frequency analysis based on the ensemble empirical mode decomposition （EEMD） method is proposed for research on the characteristics of pressure fluctuations. First, the pressure fluctuation signals are preprocessed with the empirical mode decomposition （EMD） method, and intrinsic mode functions （IMFs） are extracted. Second, the EEMD method is used to extract more precise decomposition results, and the number of iterations is determined according to the number of IMFs produced by the EMD method. Third, correlation coefficients between IMFs produced by the EMD and EEMD methods and the original signal are calculated, and the most sensitive IMFs are chosen to analyze the frequency spectrum. Finally, the operation conditions of the pump are identified with the frequency features. The results show that, compared with the EMD method, the EEMD method can improve the time-frequency resolution and extract main vibration components from pressure fluctuation signals.

A method for extracting human gait series from accelerometer signals based on the ensemble empirical mode decomposition

In this paper, the ensemble empirical mode decomposition （EEMD） is applied to analyse accelerometer signals collected during normal human walking. First, the self-adaptive feature of EEMD is utilised to decompose the ac- celerometer signals, thus sifting out several intrinsic mode functions （IMFs） at disparate scales. Then, gait series can be extracted through peak detection from the eigen IMF that best represents gait rhythmicity. Compared with the method based on the empirical mode decomposition （EMD）, the EEMD-based method has the following advantages： it remarkably improves the detection rate of peak values hidden in the original accelerometer signal, even when the signal is severely contaminated by the intermittent noises; this method effectively prevents the phenomenon of mode mixing found in the process of EMD. And a reasonable selection of parameters for the stop-filtering criteria can improve the calculation speed of the EEMD-based method. Meanwhile, the endpoint effect can be suppressed by using the auto regressive and moving average model to extend a short-time series in dual directions. The results suggest that EEMD is a powerful tool for extraction of gait rhythmicity and it also provides valuable clues for extracting eigen rhythm of other physiological signals.

Effective forecast of Northeast Pacific sea surface temperature based on a complementary ensemble empirical mode decomposition–support vector machine method

The sea surface temperature （SST） has substantial impacts on the climate; however, due to its highly nonlinear nature, evidently non-periodic and strongly stochastic properties, it is rather difficult to predict SST. Here, the authors combine the complementary ensemble empirical mode decomposition （CEEMD） and support vector machine （SVM） methods to predict SST. Extensive tests from several different aspects are presented to validate the effectiveness of the CEEMD-SVM method. The results suggest that the new method works well in forecasting Northeast Pacific SST at a 12-month lead time, with an average absolute error of approximately 0.3℃ and a correlation coefficient of 0.85. Moreover, no spring predictability barrier is observed in our experiments.

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.

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.

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.

基于CEEMD-SE的CNN&LSTM-GRU短期风电功率预测

为进一步提升短期风电功率的预测精度,提出了一种基于互补集合经验模态分解-样本熵(complementary ensemble empirical mode decomposition-sample entropy,CEEMD-SE)的卷积神经网络(convolutional neural network,CNN)和长短期记忆-门控循环单元(longshorttermmemory-gatedrecurrentunit,LSTM-GRU)的短期风电功率预测模型。首先,利用互补集合经验模态分解将原始风电功率序列分解为若干本征模态函数(intrinsic mode function,IMF)分量和一个残差(residual,RES)分量,利用样本熵算法将相近的分量进行重构;其次,搭建卷积神经网络和长短期记忆网络的并行网络结构,提取数据的局部特征和时序特征,并将特征融合后输入门控循环单元网络中进行学习预测;最后,通过算例进行验证,结果表明采用该模型后预测精度得到了有效提升,其均方根误差降低了15.06%、平均绝对误差降低了15.22%、决定系数提高了1.91%。

基于CEEMD和统计参数的斜拉桥损伤识别方法研究

为解决仅使用互补集成经验模态分解(complementary ensemble empirical mode decomposition,CEEMD)方法的斜拉桥信号分解存在含噪固有模态函数(intrinsic mode function,IMF)分量且不能进行损伤定量的问题,提出了一种基于CEEMD与统计参数方法相结合的斜拉桥损伤识别方法。该方法基于CEEMD方法对斜拉桥动力响应信号进行自适应性分解,确定适用的白噪声幅值标准差并推导CEEMD方法的集成次数,得到各阶IMF分量;采用欧氏距离对分解的IMF分量进行谱系聚类分析以避免模态混叠现象;采用峰度统计参数的有效权重峰度指标方法滤除含噪IMF分量,提取有效IMF分量并重构为有效IMF分量和;利用变异系数统计参数、二阶中心差分法和泰勒展开式推导损伤定位指标,根据四阶统计矩峰度统计参数推导损伤定量指标。用所提方法对某斜拉桥进行损伤识别研究,结果表明:仿真分析的损伤定位识别精度为100%,损伤定量最大误差为1.80%;在高斯白噪声干扰下,损伤定位不受影响,损伤定量最大误差为1.88%;进行实桥的损伤识别,结果表明实桥主梁无损伤。

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。

EEMD与LSTM在轴承剩余寿命预测中的应用

剩余使用寿命(RUL)预测是实现装备健康管理与预测性维护的最主要技术手段之一,为了准确预测轴承的剩余使用寿命,提出了一种基于集合经验模态分解(EEMD)和长短时记忆网络(LSTM)的轴承剩余寿命预测方法。首先,对采集到的振动信号做时域、频域及时频分析,同时记录相应特征;进而,筛选特征,通过EEMD对振动信号予以分解并重构;最后,通过LSTM结合经过处理的信号构建健康特征指标。通过实验证明了该方法能有效的预测出轴承的剩余寿命,且有较高的预测精度。

基于CEEMD-IDWT的受载煤岩微震电压去噪算法

受载复合煤岩变形破裂过程中产生的微小震动信号包含煤岩内部结构破裂信息,传统设备采集的微震信号存在大量环境噪声而无法直接进行分析。为有效提取受载煤岩变形破裂过程微震信号的变化特征,采用互补集合经验模态分解算法(CEEMD)与改进dmey小波(IDWT)算法相融合,提出一种新型CEEMD-IDWT联合去噪算法。该算法首先利用CEEMD算法对原始信号进行分解,然后对分解得到的IMF分量应用IDWT算法进行去噪处理,最终将处理过的分量进行重构得到去噪信号。利用仿真分析和单轴压缩实验对该算法的有效性进行验证,结果表明:CEEMD-IDWT联合算法在仿真分析中,相比传统算法信噪比最大提高204.5%,对于其他改进去噪算法信噪比最少提高11.8%,去噪能力具有明显优势;将该算法嵌入自研微震电压采集设备,在复合煤岩单轴压缩实验中得到的微震电压信号噪噪比仅为0.08975,实际去噪效果明显;经CEEMD-IDWT联合算法去噪之后的微震电压具有明显的变化特征,显著提升了信号去噪效果,有效避免了微震电压信号的失真,可以作为受载煤岩变形破裂微震电压信号去噪处理的理想算法,为煤岩动力灾害的准确预判提供了一种可靠且先进的技术参考。

CEEMD-FastICA-CWT联合瞬态响应阶次的电驱总成噪声源识别

以某增程式电驱动总成为研究对象,提出基于联合算法的噪声分离识别模型。首先,采用互补集合经验模态分解(complementary ensemble empirical mode decomposition,CEEMD)联合快速独立分量分析(fast independent component analysis,FastICA)方法提取纯电模式稳态工况下单一通道噪声信号特征,利用复Morlet小波变换及FFT对各分量信号时频特性进行识别。其次,采用阶次分析法和声能叠加法对稳态分量信号对应的各瞬态响应阶次能量进行对比分析,并结合皮尔逊积矩相关系数(Pearson product moment correlation coefficient,PPMCC)相似性识别确定不同噪声激励源贡献度。结果表明:减速齿副啮合噪声对该增程式电驱总成纯电模式运行噪声整体贡献度最大。

基于EEMD分解的阶次跟踪方法研究

摆线针轮减速器组成零部件繁多、构成复杂,工作时噪声干扰大且多在变转速、往复的复杂工况下工作,因此,难以准确提取其内部的故障特征。针对这一问题,提出了一种基于集合经验模态分解(EEMD)与阶次跟踪分析的方法,对摆线针轮减速器进行了故障诊断。首先,对采集到的时域振动信号和转速信号进行了等角度域差值采样,得到了振动信号的等角域平稳信号;然后,对等角域信号进行了集合经验模态分解,得到了若干个固有模态分量(IMFs),计算了各个固有模态分量的峭度值,选取目标模态分量进行了信号重构;接着,采用快速傅里叶变换得到了故障信号的阶次图;最后,根据减速器的传动方式、各零部件的模数,计算出了各主要部件的故障阶次,对比减速器在故障前后阶次图的能量峰值进行了故障诊断。研究结果表明:该方法能够准确提取包含故障信息的固有模态分量,实现从等时域信号到等角域信号的转换,并提取摆线针轮减速器的滚针故障阶次(8.37阶),故障准确率达到99.6%,可实现摆线针轮减速器在非平稳工况下的故障特征识别,并验证该方法的可行性和有效性。

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。

CEEMDAN和盲源分离在轴承复合故障诊断中的应用

滚动轴承的复合故障信号中往往含有多个特征信息及背景噪声,为更高效实现故障信息的提取,提出一种基于具有自适应白噪声的完备集成经验模态分解(CEEMDAN)和盲源分离的滚动轴承复合故障特征提取方法。对实验所获取的故障数据进行CEEMDAN分解,得出一组固有模态函数(IMF),利用加权峭度因子选取其中有效IMF重构信号,再将重构的信号进行BSS分离。对分离出的信号做解调包络分析,从其解调谱中提取故障信号的特征频率。结果证明了此方法可以有效地分离轴承的内外圈故障,使故障特征更易被提取。

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.

基于MEEMD算法的二冲程柴油发动机机体振动分析

二冲程低速柴油机具有复杂的振动特性,传统的经验模态分解(Empirical Mode Decomposition,EMD)算法对其振动信号处理效果不理想。为此,采用修正多元集合经验模态分解(Modified Ensemble Empirical Mode Decomposition,MEEMD)算法对低速柴油机机体振动信号进行分解。首先,采用三轴加速度计测量发动机机体振动。然后利用均方根(Root Mean Square,RMS)对三轴振动强度进行分析。最后,对x轴上的信号进行MEEMD分析。结果表明:砌块在x轴方向的振动强度最大;与EMD算法相比,MEEMD算法可以抑制模态混合,有助于更好地识别块振动激励。

基于EEMD-CNN-LSTM的新型综合模型在滑坡位移预测中的应用

滑坡位移预测是滑坡稳定性评价的重要环节。尽管基于深度学习范式的时间序列方法预测滑坡位移取得了一定的成果,但由于滑坡位移数据的非平稳性、周期性和趋势性变化特征,导致当前时间序列模型的滑坡位移的多变量预测容易过拟合。为解决这一问题,针对滑坡位移数据的波动性和由周期项与趋势项位移叠加组成的特性,提出一种基于孤立森林(Isolation Forest,IF)异常检测、集成经验模态分解(Ensemble Empirical Mode Decomposition,EEMD)、卷积神经网络(Convolutional Neural Networks,CNN)和长短期记忆神经网络(Long Short-Term Memory,LSTM)相结合的滑坡位移预测模型。选择三峡库区以降雨为影响因子的阶跃型白家包滑坡为研究对象,引入IF算法对滑坡原始位移数据进行异常检测,使用EEMD方法提取滑坡趋势项和周期项位移,通过CNN捕捉局部周期项和趋势模式,并基于LSTM模型预测总体位移。结果表明,EEMD-CNN-LSTM在预测降雨情况时滑坡总体位移的均方根误差(RMSE)、平均绝对误差(MAE)、评价绝对百分比误差(MAPE)和决定系数(R2)4种指标分别为0.4190、0.3139、0.2379和0.9997,前3种精度评价指标较现有模型分别提升32.3%、25.1%、7.3%。相较于传统的LSTM模型、随机森林方法和EEMD-LSTM方法,EEMD-CNN-LSTM模型在有、无降雨这一外部影响因素下具有显著优势,能够较大地降低过拟合,提高预测的准确性。