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Operating characteristic information extraction of flood discharge structure based on complete ensemble empirical mode decomposition with adaptive noise and permutation entropy

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JOURNAL OF VIBRATION AND CONTROL 2018年第22期24卷 5291-5301页

作者： Zhang, Jianwei Hou, Ge Ma, Bin Hua, Weiwei North China Univ Water Conservancy & Elect Power Coll Water Conservancy Zhengzhou 450011 Henan Peoples R China Tianjin Univ State Key Lab Hydraul Engn Simulat & Safety Tianjin 300072 Peoples R China

It remains a major issue to assess health condition and degree of vibration damage of flood discharge structure by working features in recent years. In the process of acquisition and transmission, because vibration signals are susceptible to interference from high-frequency white noise and low-frequency water flow noise, they are usually shown in the form of nonstationary random signals with low signal to noise ratio. Modal information is hard to be precisely recognized as the character of structural vibration is drowned into the strong noise. In order to remove the noise and preserve structural characteristic information, a new characteristic information extraction method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) entropy (CEEMDAN-PE) is proposed. Firstly, the vibration signal is decomposed into a series of intrinsic mode functions (IMFs) by CEEMDAN, and then low-frequency water flow noise can be filtered out through spectrum analysis of each IMF component. Secondly, the noise degree of each IMF is determined by permutation entropy and high-frequency noise in IMFs is filtered out by singular value decomposition. Finally, the noise elimination IMFs are reconstructed to obtain the operating characteristic information of flood discharge structure. The effectiveness of the proposed method on characteristic information extraction is validated by a simulation experiment. Furthermore, the proposed method was applied to the 5th overflow section of Three Gorges Dam and the analysis results show that the CEEMDAN-PE method can effectively remove the noise and extract dominant frequencies of flood discharge structure, which provides foundation for health monitoring and damage identification of flood discharge structure with a strong engineering practicability.

关键词： complete ensemble empirical mode decomposition with adaptive noise permutation entropy characteristic information flood discharge structure singular value decomposition

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A complete ensemble empirical mode decomposition with adaptive noise deep autoregressive recurrent neural network method for the whole life remaining useful life prediction of lithium-ion batteries

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IONICS 2023年第10期29卷 4337-4349页

作者： Zhang, Chuyan Wang, Shunli Yu, Chunmei Wang, Yangtao Fernandez, Carlos Southwest Univ Sci & Technol Sch Informat Engn Mianyang 621010 Peoples R China Sichuan Univ Coll Elect Engn Chengdu 610065 Peoples R China Robert Gordon Univ Sch Pharm & Life Sci Aberdeen AB10 7GJ Scotland

The real-time prediction of the remaining useful life (RUL) of lithium-ion batteries provides an effective mean of preventing accidents. An improved adaptive noise-reduction deep learning method is applied to achieve adaptive noise-reduction decomposition of lithium-ion battery capacity using complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and then the resulting intrinsic mode function (IMF) components are continued to be reconstructed, followed by input to a deep autoregressive recurrent neural network (DeepAR) to accurately predict the remaining useful life of lithium-ion batteries. To begin with, the lithium-ion battery data are screened and the correlation with capacity is analyzed by Pearson and Spearman to derive the indirect health factors. Then, the capacity data are decomposed by CEEMDAN to derive a relatively smooth IMF with the trend component for reconstruction, which is the output of the DeepAR model to predict the RUL of lithium-ion batteries with the indirect health factor as the output. The experimental results obtained after validation of the data set validate that the improved adaptive noise reduction DeepAR prediction model has superior prediction accuracy and greater stability, with all remaining life errors less than 5 times and all root mean square errors (RMSE) less than 2.5%.

关键词： Lithium-ion batteries Remaining useful life Health factor complete ensemble empirical mode decomposition with adaptive noise Deep autoregressive recurrent neural network

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Structural damage detection method based on the complete ensemble empirical mode decomposition with adaptive noise: a model steel truss bridge case study

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STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL 2022年第3期21卷 887-912页

作者： Mousavi, Asma Alsadat Zhang, Chunwei Masri, Sami F. Gholipour, Gholamreza Qingdao Univ Technol Sch Civil Engn Qingdao 266033 Shandong Peoples R China Univ Southern Calif Dept Civil Engn Los Angeles CA 90007 USA

Signal processing is one of the essential components in vibration-based approaches and damage detection for structural health monitoring. Since signals in the real world are often nonlinear and non-stationary, especially in extended and complex structures, such as bridges, the Hilbert-Huang transform is used for damage assessment. In recent years, the empirical mode decomposition technique has been gradually used in structural health monitoring and damage detection. In this article, the application of complete ensemble empirical mode decomposition with adaptive noise technique is investigated to identify the presence, location, and severity of damage on a steel truss bridge model. The target is built at laboratory conditions and experimentally subjected to white noise excitations. By employing complete ensemble empirical mode decomposition with adaptive noise technique, four key features extracted from the intrinsic mode functions, including energy, instantaneous amplitude, unwrapped phase, and instantaneous frequency, are assessed to localization, quantification, and detection of damage both quantitatively and qualitatively. In addition, to further explore the sensitivity of the damage detection approach based on the complete ensemble empirical mode decomposition with adaptive noise technique method, several improved damage indices are proposed based on the combinations of two statistical time-history features, including kurtosis and entropy features with the energy and instantaneous amplitude features of the analyzed signal. The experimental results from the damage indices based on the extracted features demonstrate the robustness, superiority, and more sensitivity of the complete ensemble empirical mode decomposition with adaptive noise technique method in addressing the damage location, classifying the severity, and detecting the damage compared to empirical mode decomposition and ensemble empirical mode decomposition techniques.

关键词： Structural health monitoring damage detection signal processing Hilbert-Huang transform empirical mode decomposition complete ensemble empirical mode decomposition with adaptive noise

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An Accurate QRS Complex and P Wave Detection in ECG Signals Using complete ensemble empirical mode decomposition with adaptive noise Approach

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IEEE ACCESS 2019年 7卷 128869-128880页

作者： Hossain, Md Billal Bashar, Syed Khairul Walkey, Allan J. McManus, David D. Chon, Ki H. Univ Connecticut Dept Biomed Engn Storrs CT 06269 USA Boston Univ Sch Med Dept Med Boston MA 02118 USA Univ Massachusetts Sch Med Div Cardiol Worcester MA 01655 USA

We developed a novel method for QRS complex and P wave detection in the electrocardiogram (ECG) signal. The approach reconstructs two different signals for the purpose of QRS and P wave detection from the modes obtained by the complete ensemble empirical mode decomposition with adaptive noise, taking only those modes that best represent the signal dynamics. This approach eliminates the need for conventional filtering. We first detect QRS complex locations, followed by removal of QRS complexes from the reconstructed signal to enable P wave detection. We introduce a novel method of P wave detection from both the positive and negative amplitudes of the ECG signal and an adaptive P wave search approach to find the true P wave. Our detection method automatically identifies P waves without prior information. The proposed method was validated on two well-known annotated databases-the MIT BIH Arrythmia database (MITDB) and The QT database (QTDB). The QRS detection algorithm resulted in 99.96% sensitivity, 99.9% positive predictive value, and an error of 0.13% on all validation databases. The P wave detection method had better performance when compared to other well-known methods. The performance of our P wave detection on the QTDB showed a sensitivity of 99.96%, a positive predictive value of 99.47%, and the mean error in P peak detection was less than or equal to one sample (4 ms) on average.

关键词： complete ensemble empirical mode decomposition with adaptive noise CEEMDAN ECG QRS complex P wave signal reconstruction

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Damage detection and characterization of a scaled model steel truss bridge using combined complete ensemble empirical mode decomposition with adaptive noise and multiple signal classification approach

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STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL 2022年第4期21卷 1833-1848页

作者： Mousavi, Asma A. Zhang, Chunwei Masri, Sami F. Gholipour, Gholamreza Qingdao Univ Technol Sch Civil Engn 11 Fushun Rd Qingdao 266033 Peoples R China Univ Southern Calif Dept Civil & Environm Engn Los Angeles CA 90007 USA

This study aims to investigate the performance of a new damage detection method proposed based on the combination of two signal processing techniques which are complete ensemble empirical mode decomposition with adaptive noise and multiple signal classification (CEEMDAN-MUSIC). The proposed damage detection approach begins with determining the power density spectrum, namely, the pseudospectrum, from the acceleration response of a structure. Then, the CEEMDAN algorithm is used to decompose the vibration signal into a set of intrinsic mode functions (IMFs). Furthermore, the MUSIC algorithm is applied to the first IMF of the processed signal to determine the frequency pseudospectrum, prior to and post the damage states of the structure. The effectiveness of the proposed methodology is experimentally validated using a laboratory-scale model of a steel truss bridge exposed to a white noise excitation. The damage states of the truss bridge are implemented by replacing a specified diagonal element with reduced cross-sectional stiffness. The experimental results demonstrate the superiority of the CEEMDAN-MUSIC method in comparison with the performance of pure MUSIC and traditional frequency domain techniques. The advantages of the proposed technique are also discussed in terms of identifying the presence of the damage, addressing its location, and quantifying the damage levels which are summarized as the damage detection and characterization.

关键词： Damage detection damage characterization signal processing bridge model testing complete ensemble empirical mode decomposition with adaptive noise multiple signal classification

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Study on the Improvement of the Application of complete ensemble empirical mode decomposition with adaptive noise in Hydrology Based on RBFNN Data Extension Technology

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Computer modeling in Engineering & Sciences 2021年第2期126卷 755-770页

作者： Jinping Zhang Youlai Jin Bin Sun Yuping Han Yang Hong School of Water Conservancy Engineering Zhengzhou UniversityZhengzhou450001China Yellow River Institute for Ecological Protection&Regional Coordinated Development Zhengzhou UniversityZhengzhou450001China North China University of Water Resources and Electric Power Zhengzhou450046China School of Civil Engineering and Environmental Sciences University of OklahomaNormanOK73019USA

The complex nonlinear and non-stationary features exhibited in hydrologic sequences make hydrological analysis and forecasting ***,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 ***,the endpoint effect in CEEMDAN is often neglected,which can lead to decomposition errors that reduce the accuracy of the research *** 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 ***,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 *** results indicated that the RBFNN extension technique effectively reduced the error of medium and low frequency components caused by the endpoint *** both ends of the components,the extension sequence more accurately reflected the true fluctuation characteristics and variation *** advances are of great significance to the subsequent study of ***,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.

关键词： complete ensemble empirical mode decomposition with adaptive noise data extension radial basis function neural network multi-time scales runoff

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Fault diagnosis of horizontal centrifugal pump orifice ring wear and blade fracture based on complete ensemble empirical mode decomposition with adaptive noise-singular value decomposition algorithm

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JOURNAL OF VIBRATION AND CONTROL 2024年第23-24期30卷 5228-5236页

作者： Lin, Bin Zhu, Rongsheng Huang, Qian Zhang, Yongyong Fu, Qiang Wang, Xiuli Jiangsu Univ Natl Res Ctr Pumps Zhenjiang 212001 Peoples R China Joint Lab Intelligent Diag Operat & Maintenance Nu Zhenjiang Peoples R China China Nucl Power Engn Co Ltd Beijing Peoples R China

Horizontal centrifugal pump orifice ring wear and blade fracture failure will not only affect the hydraulic performance but also affect the safety and stability of the whole unit. In this paper, the horizontal centrifugal pump orifice ring wear and blade fracture failure are studied, and carry out condition monitoring and fault identification through the vibration signal under the failure. Combined with the complete ensemble empirical mode decomposition with adaptive noise and Singular Value decomposition algorithm of adaptive noise, a vibration feature extraction method of horizontal centrifugal pump based on intrinsic mode singular value is proposed. Through the BP neural network, based on the time domain, frequency domain, wavelet packet-AR spectrum, and intrinsic mode singular value characteristics of single-point and double-point vibration, the identification model is constructed and the identification effect is compared. The research shows that the vibration feature recognition effect of CEEMDAN-SVD decomposition is verified based on BP neural network model, and the BP neural network is improved by Particle Swarm Optimization to further improve the recognition effect and speed, which provides the diagnosis model for the design of subsequent diagnosis system.

关键词： Orifice ring wear blade fracture complete ensemble empirical mode decomposition with adaptive noise singular value decomposition

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A Hybrid model Based on complete ensemble empirical mode decomposition with adaptive noise, GRU Network and Whale Optimization Algorithm for Wind Power Prediction

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IEEE ACCESS 2023年 11卷 62840-62854页

作者： Sheng, Andi Xie, Lewei Zhou, Yixiang Wang, Zhen Liu, Yuechao North China Elect Power Univ Comp Dept Baoding 071000 Hebei Peoples R China Huazhong Agr Univ Coll Sci Wuhan 430000 Hubei Peoples R China Anhui Med Univ Sch Hlth Adm Hefei 230012 Anhui Peoples R China Zhejiang Yuexiu Univ Hotel Management Dept Shaoxing 312000 Zhejiang Peoples R China North China Elect Power Univ Dept Math & Phys Baoding 071003 Hebei Peoples R China

To ensure the safe and stable operation of power systems, accurate prediction of wind power generation is particularly important. However, due to the randomness, fluctuation, and intermittency of wind energy, as well as the challenges in determining the hyperparameters of the gated recurrent unit (GRU) network, this paper proposes an innovative "decomposition-optimization-reconstruction" prediction method. To enhance the accuracy of wind power prediction, a sophisticated wind power hybrid prediction model based on the GRU network, combined with complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and the whale optimization algorithm (WOA) has been proposed in this paper. Among the efficient decomposition algorithms are successive variational mode decomposition (SVMD) and CEEMDAN. Taking into account adaptability issues, in the data decomposition stage, the CEEMDAN method is chosen to decompose the wind power time series into different sub-sequence components. This approach allows for a better representation of the fluctuation characteristics of each sub-sequence at various time scales. In the prediction optimization stage, the GRU prediction method is used to predict each sub-sequence component and the WOA algorithm is combined to optimize the hyperparameters of each GRU. This approach demonstrates significant advantages in improving wind power prediction accuracy, enhancing generalization capability, and strengthening adaptability. In the prediction reconstruction stage, the prediction results of each sub-sequence component are superimposed to obtain the final wind power prediction value. Finally, the model is verified using actual wind power data from a power plant in the northwest, and the simulation results show that the wind power prediction model based on CEEMDAN-WOA-GRU has significant advantages in prediction accuracy and stability compared to other models. This model provides strong support for optimizing wind energy grid integration

关键词： decomposition-optimization-reconstruction gated recurrent unit complete ensemble empirical mode decomposition with adaptive noise whale optimization algorithm wind power prediction

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A hybrid approach based on complete ensemble empirical mode decomposition with adaptive noise for multi-step-ahead solar radiation forecasting

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Clean Energy 2022年第5期6卷 705-715页

作者： Khaled Ferkous Tayeb Boulmaiz Fahd Abdelmouiz Ziari Belgacem Bekkar Materials Energy Systems Technology and Environment LaboratoryGhardaia UniversityGhardaiaAlgeria Laboratory of Mathematical and Subatomic Physics Department of PhysicsFaculty of Exact SciencesUniversity of Constantine 1Algeria

Accurate measurements of solar radiation are required to ensure that power and energy systems continue to function effectively and *** the other hand,estimating it is extremely challenging due to the non-stationary behaviour and randomness of its *** this research,a novel hybrid forecasting model,namely complete ensemble empirical mode decomposition with adaptive noise-Gaussian process regression(CEEMDAN-GPR),has been developed for daily global solar radiation *** non-stationary global solar radiation series is transformed by CEEMDAN into regular *** that,the GPR model uses these subsets as inputs to perform its *** to the results of this research,the performance of the developed hybrid model is superior to two widely used hybrid models for solar radiation forecasting,namely wavelet-GPR and wavelet packet-GPR,in terms of mean square error,root mean square error,coefficient of determination and relative root mean square error values,which reached 3.23 MJ/m^(2)/day,1.80 MJ/m^(2)/day,95.56%,and 8.80%,respectively(for one-step forward forecasting).The proposed hybrid model can be used to ensure the safe and reliable operation of the electricity system.

关键词： hybrid models complete ensemble empirical mode decomposition with adaptive noise Gaussian process regression prediction solar measurements Ghardaia site

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Digital image stabilization in mountain areas using complete ensemble empirical mode decomposition with adaptive noise and structural similarity

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JOURNAL OF ELECTRONIC IMAGING 2016年第3期25卷 033007-033007页

作者： Hao, Duo Li, Qiuming Li, Chengwei Harbin Inst Technol Sch Elect Engn & Automat 92 Xidazhi St Harbin 150001 Peoples R China Harbin Inst Technol Sch Astronaut 2 Yikuang St Harbin 150001 Peoples R China

Cameras mounted on scouting vehicles frequently suffer from image shake because of unintentional motions. Image shake is a main source of inaccuracies that lead to bad scouting results, particularly in mountain areas with complicated terrains. To overcome this disadvantage, this study proposes a digital image stabilization method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and structural similarity (SSIM) to generate a stable scouting video sequence. The proposed method first calculates the global motion vector (GMV) from a scouting video sequence using the bit-plane matching algorithm. To separate jitter motion from intentional motion, we decompose GMV into several modes using CEEMDAN. Then according to different structural characteristics, SSIM is used to draw a boundary among modes to separate jitter motion from intentional motion. To evaluate stabilization performance in complicated situations, several known methods and the proposed stabilization method are compared. Experimental results show that CEEMDAN outperforms the other stabilization methods under mountain area conditions. (C) 2016 SPIE and IS&T

关键词： mountain area digital image stabilization complete ensemble empirical mode decomposition with adaptive noise structural similarity

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