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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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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Fault Feature Extraction of Hydraulic Pumps Based on Symplectic Geometry Mode Decomposition and Power Spectral Entropy [PDF]
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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Roller Bearing Fault Diagnosis Based on Partial Reconstruction Symplectic Geometry Mode Decomposition and LightGBM [PDF]
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
doaj +3 more sources
Marine Controlled-Source Electromagnetic Data Denoising Method Using Symplectic Geometry Mode Decomposition [PDF]
Journal of Marine Science and Engineering, 2023 The marine controlled-source electromagnetic (CSEM) method is an efficient tool for hydrocarbon exploration. The amplitudes of signals decay rapidly with the increasing offset, so signals are easily contaminated by various kinds of noise.
Yijie Chen, Zhenwei Guo, Dawei Gao
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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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An early fault diagnosis method of gear based on improved symplectic geometry mode decomposition
Measurement, 2019 Abstract Symplectic geometry mode decomposition (SGMD) is an effective signal processing method, and it has been applied in compound fault diagnosis successfully. However, for early gear fault vibration signals, SGMD has two shortcomings. On the one hand, SGMD directly reconstructs the trajectory matrix through the original time series, which may ...
Jian Cheng +4 more
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A Fault Diagnosis Approach for Rolling Bearing Integrated SGMD, IMSDE and Multiclass Relevance Vector Machine [PDF]
Sensors, 2020 The vibration signal induced by bearing local fault has strong nonstationary and nonlinear property, which indicates that the conventional methods are difficult to recognize bearing fault patterns effectively.
Xiaoan Yan, Ying Liu, Minping Jia
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Partial Discharge Fault Diagnosis in Power Transformers Based on SGMD Approximate Entropy and Optimized BILSTM [PDF]
EntropyPartial discharge (PD) fault diagnosis is of great importance for ensuring the safe and stable operation of power transformers. To address the issues of low accuracy in traditional PD fault diagnostic methods, this paper proposes a novel method for the ...
Haikun Shang +3 more
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Early Fault Diagnosis of Bearings Based on Symplectic Geometry Mode Decomposition Guided by Optimal Weight Spectrum Index [PDF]
SymmetryWhen the rotating machinery fails, the signal generated by the faulty component often no longer maintains the original symmetry, which makes the vibration signal with nonlinear and non-stationary characteristics, and is easily affected by background noise and other equipment excitation sources.
Chenglong Wei +3 more
openalex +2 more sources