Study on Noise Reduction of Hydrostatic Leveling Signals for Wind Turbine Foundations Based on CEEMDAN-SG Algorithm by Renjie Li, Xiangxing Lu, Zhixin Song, Huanwei Wei, Fang Tan, Zhonghua Liu

“…The hydrostatic leveling monitoring data related to the settlement of the wind turbine foundation display substantial fluctuations along with considerable noise. In this study, based on the characteristic of the hydrostatic level measurement data of wind turbine foundation, a joint denoising method that integrates the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) algorithm with Savitzky–Golay (SG) filtering is proposed. …”
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Research on de-noising method of optical fiber grating sensing signal based on S-G and CEEMDAN technology by HUANG Gang, PAN Like, HE Chengzhang, YANG Chengfeng, LI Zhong, GONG Shaokang

“…Aiming at the problems of low accuracy and poor reliability of existing signal de-noising methods in the signal processing of fiber Bragg grating sensing system, based on the principle of fiber Bragg grating sensing, a signal de-noising method of fiber Bragg grating sensing system combining complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method and S-G filtering method is proposed. …”
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CPO-VMD Combined With Multiscale Permutation Entropy for Noise Reduction in GNSS Vertical Time Series in Mining Areas by Xu Yang, Xinxin Yao, Xinjian Fang, Xuexiang Yu, Yi Wu, Shicheng Xie

“…The results of simulation and example analysis show that compared with wavelet denoising (WD), empirical modal decomposition (EMD), ensemble empirical mode decomposition (EEMD), and improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), this method shows significant advantages in the evaluation indexes of noise reduction effect—Pearson’s correlation coefficient (R), the signal-to-noise ratio (SNR), and the root-mean-square error (RMSE)—and all the indexes are better than the comparative methods. …”
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Research on Electric Vehicle Charging Load Prediction Methods Combining Signal Noise Reduction and Time Series Modeling by Liyun Liu, Xiaomei Xu, Jinsong Zhang, Dong Li

“…This study introduces a hybrid deep learning model combining Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Convolutional Neural Networks (CNN), Bi-directional Gated Recurrent Units (BiGRU), and Attention Mechanism (AM) to address the volatility in charging load patterns. …”
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Research on Fault Diagnosis Method of High-Speed EMU Air Compressor Based on ICEEMDAN and Wavelet Threshold Combined Noise Reduction by Liqiang Peng, Akang Guo, Shuzhao Zhang

“…In response to this problem, this paper proposes a high-speed train air compressor fault diagnosis method based on an improved complete ensemble empirical mode decomposition adaptive noise (ICEEMDAN) and t-Distributed Stochastic Neighbor Embedding (t-SNE) algorithm. …”
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Research on a hybrid deep learning model based on two-stage decomposition and an improved whale optimization algorithm for air quality index prediction by Hangyu Zhou, Yongquan Yan

“…A hybrid deep learning model is developed for AQI prediction, incorporating two-stage decomposition and hyperparameter optimization. The two-stage decomposition method first uses Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) to initially decompose the AQI sequences, followed by Singular Spectrum Analysis (SSA) applied to the subsequence with the highest Weighted Permutation Entropy (WPE), and all subsequences are then reconstructed. …”
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Short-term prediction of trimaran load based on data driven technology by Haoyun Tang, Rui Zhu, Qian Wan, Deyuan Ren

“…To highlight the trimaran high-frequency load fluctuation and improve the prediction accuracy, the LSTM neural network combines with different signal decomposition algorithms, such as Empirical Mode Decomposition (EMD), Ensemble Empirical Mode Decomposition (EEMD), Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), and Variational Mode Decomposition (VMD). …”
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Mode Shape Extraction with Denoising Techniques Using Residual Responses of Contact Points of Moving Vehicles on a Beam Bridge by Guandong Qiao, Xiaoyue Du, Qi Wang, Liu Jiang

“…A comprehensive investigation is conducted on several critical parameters, including window size, vehicle velocity, road roughness, and beam damping property, as well as the influence of traffic flow. To enhance the mode shape extraction performance using the approximate expression of the contact points’ displacements under noisy disturbance, two new signal denoising methods, CEEMDAN-NSPCA and CEEMDAN-IWT, are proposed based on complete ensemble empirical mode decomposition (CEEMDAN). …”
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Real-time damage detection of bridges using adaptive time-frequency analysis and ANN by V. Ahmadian, S. B. Beheshti Aval, E. Darvishan

“…First, three adaptive signal processing techniques including Empirical Mode Decomposition (EMD), Local Mean Decomposition (LMD) and Hilbert Vibration Decomposition (HVD) are compared. …”
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RESEARCH OF EARLY FAULT FEATURE EXTRACTION OF SOLAR WHEEL BASED ON PARAMETRIC ADAPTIVE ICEEMDAN AND MCKD by ZHAO Naizhuo, ZHAO Yumeng, MEN Chengfu

“…In order to solve the problem of difficult to accurately extract early faults of solar wheels under the strong noise background, an improved grey wolf algorithm (newGWO) was proposed to optimize and improve the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and the maximum correlated kurtosis deconvolution (MCKD) for early fault feature extraction of solar wheels.NewGWO was used to optimize the selection of parameters of the white noise amplitude weight and noise addition times that affected the decomposition effect.The fault vibration signal was decomposed by newGWO-ICEEMDAN, and the minimum envelope entropy was selected as the fitness function to obtain several related modal components.Then, the envelope spectrum peak factor was selected as the best modal component index.MCKD signals optimized by newGWO were enhanced for the selected optimal intrinsic mode function （IMF） components. …”
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Reservoir water level prediction using combined CEEMDAN-FE and RUN-SVM-RBFNN machine learning algorithms by Lan-ting Zhou, Guan-lin Long, Can-can Hu, Kai Zhang

“…This study proposed a method for reservoir water level prediction based on CEEMDAN-FE and RUN-SVM-RBFNN algorithms. By integrating the adaptive complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method and fuzzy entropy (FE) with the new and highly efficient Runge–Kuta optimizer (RUN), adaptive parameter optimization for the support vector machine (SVM) and radial basis function neural network (RBFNN) algorithms was achieved. …”
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BWO–ICEEMDAN–iTransformer: A Short-Term Load Forecasting Model for Power Systems with Parameter Optimization by Danqi Zheng, Jiyun Qin, Zhen Liu, Qinglei Zhang, Jianguo Duan, Ying Zhou

“…First, the parameters of improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) were optimized using beluga whale optimization (BWO). …”
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GNSS Signal Extraction Using CEEMDAN–WPD for Deformation Monitoring of Ropeway Pillars by Song Zhang, Yuntao Yang, Yilin Xie, Haoran Tang, Haiyang Li, Lianbi Yao, Yin Yang

“…However, effective signal extraction from GNSS data remains a challenging task due to the presence of noise and complex signal components. This study integrates Complementary Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and wavelet packet decomposition (WPD) to extract GNSS deformation monitoring signals for the ropeway pillar. …”
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Denoising of acoustic emission signals from rock failure processes through ICEEMDAN combined with multiple criteria and wavelet transform by Tao Wang, Weiwei Ye, Liyuan Liu, Zhihui Zhao, Wei Huang

“…Combining laboratory experiments on rock AE and theoretical research on signal denoising, a denoising method based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) is proposed for rock fracture AE signals. …”
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Surface Roughness Prediction of Bearing Ring Precision Grinding Based on Feature Extraction by Chaoyu Shi, Bohao Chen, Yao Shi, Jun Zha

“…Firstly, the signal was decomposed using the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) algorithm, and the sensitive components were selected based on the correlation coefficient between Intrinsic Mode Functions (IMFs) and the original signal. …”
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Application of a Hybrid Model Based on CEEMDAN and IMSA in Water Quality Prediction by GUO Li-jin, WU Hao-tian

“…[Methods] First, the original water quality sequence was decomposed using Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN). …”
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From Signal to Safety: A Data-Driven Dual Denoising Model for Reliable Assessment of Blasting Vibration Impacts by Miao Sun, Jing Wu, Junkai Yang, Li Wu, Yani Lu, Hang Zhou

“…Firstly, it applies endpoint processing (EP) to the signal, followed by complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to suppress low-frequency clutter. …”
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Short-Term Wind Power Prediction Method Based on CEEMDAN-VMD-GRU Hybrid Model by Na Fang, Zhengguang Liu, Shilei Fan

“…In order to improve wind power prediction accuracy and increase the utilization of wind power, this study proposes a novel complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN)–variational modal decomposition (VMD)–gated recurrent unit (GRU) prediction model. …”
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Monthly Runoff Prediction Based on STL-CEEMDAN-LSTM Model by WANG Hai, SHEN Yanqing, QI Shansheng, PAN Hongzhong, HUO Jianzhen, WANG Zhance

“…The complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm was then applied to decompose the residual terms to obtain intrinsic mode functions (IMFs) of different frequency components. …”
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Dynamic Error Modeling and Predictive Compensation for Direct-Drive Turntables Based on CEEMDAN-TPE-LightGBM-APC Algorithm by Manzhi Yang, Hao Ren, Shijia Liu, Bin Feng, Juan Wei, Hongyu Ge, Bin Zhang

“…Our methodology comprises four key stages: Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN)-based decomposition of historical error data, development of component-specific prediction models using Tree-structured Parzen Estimator (TPE)-optimized Light Gradient Boosting Machine (LightGBM) algorithms for each Intrinsic Mode Function (IMF), integration of component predictions to generate initial values, and application of the Adaptive Prediction Correction (APC) module to produce final predictions. …”
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