A novel denoising method for non‐linear and non‐stationary signals by Honglin Wu, Zhongbin Wang, Lei Si, Chao Tan, Xiaoyu Zou, Xinhua Liu, Futao Li

“…First, an improved VMD method is used to decompose the original signal into an optimal number of intrinsic mode functions (IMFs). Second, the energy variation ratio function is applied to distinguish between the effective and non‐effective IMFs. …”
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A New Decomposition Ensemble Learning Approach with Intelligent Optimization for PM2.5 Concentration Forecasting by Guangyuan Xing, Shaolong Sun, Jue Guo

“…First, we utilize EEMD to decompose original time series of PM2.5 concentrations into a specific amount of independent intrinsic mode functions (IMFs) and residual term. Second, the ANN, whose connection parameters are optimized by APSO algorithm, is employed to model IMFs and residual terms, respectively. …”
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A Morphology Filter-Assisted Extreme-Point Symmetric Mode Decomposition (MF-ESMD) Denoising Method for Bridge Dynamic Deflection Based on Ground-Based Microwave Interferometry by Xianglei Liu, Mengzhuo Jiang, Ziqi Liu, Hui Wang

“…First, the original bridge dynamic deflection obtained with ground-based microwave interferometry was decomposed to obtain a series of intrinsic mode functions (IMFs) with the ESMD method. Second, the noise-dominant IMFs were removed according to Spearman’s rho algorithm, and the other decomposed IMFs were reconstructed as a new signal. …”
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Forecasting Uranium Resource Price Prediction by Extreme Learning Machine with Empirical Mode Decomposition and Phase Space Reconstruction by Qisheng Yan, Shitong Wang, Bingqing Li

“…In the first stage, the original uranium resource price series are first decomposed into a finite number of independent intrinsic mode functions (IMFs), with different frequencies. In the second stage, the IMFs are composed into three subseries based on the fine-to-coarse reconstruction rule. …”
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Fault Feature Extraction and Diagnosis of Gearbox Based on EEMD and Deep Briefs Network by Kai Chen, Xin-Cong Zhou, Jun-Qiang Fang, Peng-fei Zheng, Jun Wang

“…The original data is decomposed into a set of intrinsic mode functions (IMFs) using EEMD, and then main IMFs were chosen for reconstructed signal to suppress abnormal interference from noise. …”
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A Bearing Performance Degradation Modeling Method Based on EMD-SVD and Fuzzy Neural Network by Jingbo Gai, Yifan Hu, Junxian Shen

“…Firstly, the vibration signals of bearings in known states were decomposed by empirical mode decomposition (EMD) to obtain the intrinsic mode functions (IMFs) containing feature information. Then, the selected key IMFs which contain the main features were decomposed by singular value decomposition (SVD). …”
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Internal Leakage Diagnosis of a Hydraulic Cylinder Based on Optimization DBN Using the CEEMDAN Technique by Peng Zhang, Xinyuan Chen

“…The raw AE signals are decomposed into a set of intrinsic mode functions (IMFs) by using CEEMDAN. Subsequently, according to the decreasing order of the Pearson correlation coefficient values, the first five IMFs are selected for signal reconstruction to suppress the abnormal interference from noise. …”
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Fault Diagnosis of Electromechanical Actuator Based on VMD Multifractal Detrended Fluctuation Analysis and PNN by Hongmei Liu, Jiayao Jing, Jian Ma

“…First, the vibration signals were decomposed by VMD into a number of intrinsic mode functions (IMFs). Second, the multifractal features hidden in IMFs were extracted by using MFDFA, and the generalized Hurst exponents were selected as the feature vectors. …”
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Wind speed prediction model based on multiscale temporal‐preserving embedding broad learning system by Jiayi Qiu, Yatao Shen, Ziwen Gu, Zijian Wang, Wenmei Li, Ziqian Tao, Ziwen Guo, Yaqun Jiang, Chun Huang

“…Firstly, frequency clustering‐based variational mode decomposition (FC‐VMD) is proposed to deal with the non‐stationary wind speed data into multiple intrinsic mode functions (IMFs). Then, temporal‐preserving embedding (TPE) is proposed to extract the underlying temporal manifold structure from the decomposed IMFs. …”
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An Improved Time-Frequency Analysis Method for Instantaneous Frequency Estimation of Rolling Bearing by Zengqiang Ma, Wanying Ruan, Mingyi Chen, Xiang Li

“…Firstly, the signal is decomposed into several intrinsic mode functions (IMFs) with different center frequency by VMD. Then, effective IMFs are selected by mutual information and kurtosis criteria and are reconstructed. …”
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The Behavioral Mechanism and Forecasting of Beijing Housing Prices from a Multiscale Perspective by Yan Li, Zhaoyang Xiang, Tao Xiong

“…Then, we compose the IMFs and residual into three components caused by normal market disequilibrium, extreme events, and the economic environment using the fine-to-coarse reconstruction algorithm. …”
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A structural health monitoring data reconstruction method based on VMD and SSA-optimized GRU model by Xiaoliang Jia, Guoyan Zhang, Zhiqiang Wang, Huacong Li, Jing Hu, Songlin Zhu, Caiwei Liu

“…The methodology initially employs Variational Mode Decomposition (VMD) to preprocess and decompose the existing data from the target sensor into Intrinsic Mode Functions (IMFs) and residuals. Subsequently, the Gated Recurrent Unit (GRU) network utilizes data from other sensors to reconstruct the IMFs and residuals, ultimately producing the data reconstruction results. …”
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Research on Feature Extracted Method for Flutter Test Based on EMD and CNN by Hua Zheng, Zhenglong Wu, Shiqiang Duan, Jiangtao Zhou

“…The IMFs are then reshaped to make them the suitable size to be input to the CNN. …”
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Gear Fault Detection Based on Teager-Huang Transform by Hui Li, Haiqi Zheng, Liwei Tang

“…EMD can adaptively decompose the vibration signal into a series of zero mean Intrinsic Mode Functions (IMFs). TKEO can track the instantaneous amplitude and instantaneous frequency of the Intrinsic Mode Functions at any instant. …”
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Assessing Nonlinear Dynamics and Trends in Precipitation by Ensemble Empirical Mode Decomposition (EEMD) and Fractal Approach in Benin Republic (West Africa) by Médard Noukpo Agbazo, Gabin Koto N’Gobi, Eric Alamou, Basile Kounouhewa, Abel Afouda

“…Intrinsic Mode Functions (IMFs) are obtained according to the climatic region in which the stations are located. …”
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Application of EMD-Based SVD and SVM to Coal-Gangue Interface Detection by Wei Liu, Kai He, Qun Gao, Cheng-yin Liu

“…Due to the nonstationary characteristics in vibration signals of the tail boom support of the longwall mining machine in this complicated environment, the empirical mode decomposition (EMD) is used to decompose the raw vibration signals into a number of intrinsic mode functions (IMFs) by which the initial feature vector matrices can be formed automatically. …”
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EEMD-MUSIC-Based Analysis for Natural Frequencies Identification of Structures Using Artificial and Natural Excitations by David Camarena-Martinez, Juan P. Amezquita-Sanchez, Martin Valtierra-Rodriguez, Rene J. Romero-Troncoso, Roque A. Osornio-Rios, Arturo Garcia-Perez

“…The EEMD and MUSIC methods are used to decompose the vibration signal into a set of IMFs (intrinsic mode functions) and to identify the natural frequencies of a structure, respectively. …”
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Feature Extraction Strategy with Improved Permutation Entropy and Its Application in Fault Diagnosis of Bearings by Fan Jiang, Zhencai Zhu, Wei Li, Bo Wu, Zhe Tong, Mingquan Qiu

“…In this method, ensemble empirical mode decomposition (EEMD), a self-adaptive time-frequency analysis method, is used to process the vibration signals, and a set of intrinsic mode functions (IMFs) can thus be obtained. A feature extraction strategy based on statistical analysis is then presented for IPE, where the so-called optimal number of permutation entropy (PE) values used for an IPE is adaptively selected. …”
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Noise Source Separation of an Internal Combustion Engine Based on a Single-Channel Algorithm by Jiachi Yao, Yang Xiang, Sichong Qian, Shuai Wang

“…Firstly, the TVF-EMD method is utilized to decompose the single-channel noise signal into several intrinsic mode functions (IMFs). Then, the RobustICA method is applied to extract the independent components. …”
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Short-Term Passenger Flow Forecasting for Rail Transit considering Chaos Theory and Improved EMD-PSO-LSTM-Combined Optimization by Lixin Zhao, Hui Jin, Xintong Zou, Xiao Liu

“…This paper proposes a prediction method based on chaos theory and an improved empirical-modal-decomposition particle-swarm-optimization long short-term-memory (EMD-PSO-LSTM)-combined optimization process for passenger flow data with high nonlinearity and dynamic space-time dependence, using EMD to process the original passenger flow data and generate several eigenmodal functions (IMFs) and residuals with different characteristic scales. …”
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