Comprehensive Separation Algorithm for Single-Channel Signals Based on Symplectic Geometry Mode Decomposition [PDF]
SensorsThis paper aims to explore the difficulty of obtaining source signals from complex mixed signals and the issue that the FastICA algorithm cannot directly decompose the received single-channel mixed signals and distort the signal separation in low signal ...
Xinyu Wang, Jin Zhao, Xianliang Wu
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The Partial Reconstruction Symplectic Geometry Mode Decomposition and Its Application in Rolling Bearing Fault Diagnosis [PDF]
Sensors, 2023 Extracting the fault characteristic information of rolling bearings from intense noise disturbance has been a heated research issue. Symplectic geometry mode decomposition (SGMD) has already been adopted for bearing fault diagnosis due to its advantages ...
Yanfei Liu +5 more
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Train Axlebox Bearing Fault Diagnosis Based on MSC–SGMD [PDF]
Sensors, 2023 Train axlebox bearings are subject to harsh service conditions, and the difficulty of diagnosing compound faults has brought greater challenges to the maintenance of high–quality train performance.
Yongliang Bai, Hai Xue, Jiangtao Chen
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Symplectic Foliation Structures of Non-Equilibrium Thermodynamics as Dissipation Model: Application to Metriplectic Nonlinear Lindblad Quantum Master Equation [PDF]
Entropy, 2022 The idea of a canonical ensemble from Gibbs has been extended by Jean-Marie Souriau for a symplectic manifold where a Lie group has a Hamiltonian action.
Frédéric Barbaresco
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Complex Time Approach to the Hamiltonian and the Entropy Production of the Damped Harmonic Oscillator [PDF]
EntropyThe present work applies and extends the previously developed Quantitative Geometrical Thermodynamics (QGT) formalism to the derivation of a Hamiltonian for the damped harmonic oscillator (DHO) across all damping regimes.
Kyriaki-Evangelia Aslani
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Entropy, 2019 Aiming at fault feature extraction of a hydraulic pump signal, a new method based on symplectic geometry mode decomposition (SGMD) and power spectral entropy (PSE) is proposed.
Zhi Zheng, Ge Xin
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IEEE Access, 2023 It is always a hot and challenging problem to extract the characteristic information of roller bearings from strong noise interference. Conventional Hilbert-Huang Transform (HHT), Local Mean Decomposition (LMD), Local Feature-Scale Decomposition (LCD ...
Yanfei Liu +5 more
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Symplectic Entropy as a Novel Measure for Complex Systems
Entropy, 2016 Real systems are often complex, nonlinear, and noisy in various fields, including mathematics, natural science, and social science. We present the symplectic entropy (SymEn) measure as well as an analysis method based on SymEn to estimate the ...
Min Lei +4 more
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Feature Extraction of Gearbox Early Fault based on ISGMD and MED
Jixie chuandong, 2022 Aiming at the difficulty in identifying early faults and compound faults of gearboxes under strong noise background，a method of extracting fault features based on the combination of improved symplectic geometry mode decomposition （ISGMD） and minimum ...
Shuzhou Dong, Xunpeng Qin, Shiming Yang
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RESEARCH ON ROLLING BEARING FAULT FEATURE EXTRACTION METHOD WITH SGMD-MOMEDA (MT)
Jixie qiangdu, 2022 Aiming at the problem that the vibration signal of rolling bearing is difficult to extract due to the characteristics of non-linear, non-stationary and low signal-to-noise ratio, a new fault extraction method based on symplectic geometry mode ...
CAO YaLei +5 more
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