Study on the Rolling Bearing Fault Diagnosis based on the Hilbert Envelope Spectrum Singular Value and IPSO-SVM by Qin Bo, Sun Guodong, Zhang Liqiang, Liu Yongliang, Zhang Chao, Wang Jianguo

“…Firstly,the rolling bearing signal is divided by EMD,it selects IMFs that contains main characteristics of signal for Hilbert demodulation envelope analysis to obtain envelope matrix and the singular value decomposition is carried out. …”
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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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Composite fault feature extraction for gears based on MCKD-EWT adaptive wavelet threshold noise reduction by Yanchang LV, Jingyue Wang, Chengqiang Zhang, Jianming Ding

“…Firstly, envelope entropy and information entropy are used as fitness functions, and the parameters of the MCKD algorithm are optimized by the improved particle swarm algorithm, then the empirical wavelet decomposition is carried out on the signals, and finally adaptive wavelet threshold denoising is carried out on the decomposed Intrinsic mode functions (IMFs) components. The results of experimental data analysis show that compared with the feature extraction methods such as spatial scale threshold EWT-MCKD and Complete Ensemble Empirical Mode Decomposition (CEEMDAN)-MCKD, the proposed method is more suitable for the diagnosis of gear composite faults in a strong background noise environment, the noise interference is effectively suppressed, and the extraction effect of gear composite fault features is more obvious.…”
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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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A novel hybrid methodology for wind speed and solar irradiance forecasting based on improved whale optimized regularized extreme learning machine by S. Syama, J. Ramprabhakar, R Anand, V. P. Meena, Josep M. Guerrero

“…Further, permutation entropy is employed to extract the complexity of IMFs for filtering and reconstruction of decomposed components to alleviate the difficulty of direct modeling. …”
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A Hybrid Dynamic Principal Component Analysis Feature Extraction Method to Identify Piston Pin Wear for Binary Classifier Modeling by Hao Yang, Yubin Zhai, Mengkun Zheng, Tan Wang, Dongliang Guo, Jianhui Liang, Xincheng Li, Xianliang Liu, Mingtao Jia, Rui Zhang

“…Then, the dynamic principal component matrix is further decomposed by VMD to obtain intrinsic mode functions (IMFs) as finer features and is finally decomposed by SVD to compress the features, thus improving the classification efficiency based on the features. …”
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