Results 21 to 30 of about 7,212 (303)

Feature cognitive model combined by an improved variational mode and singular value decomposition for fault signals

Cognitive Computation and Systems, 2020
A feature cognitive model combined with an improved variational mode and singular value decomposition is presented to recognise the characteristics of the fault signals from vibration signals of mechanical equipment in this study.
Jinxiang Chen, Zhu Zhu, Xiaoda Zhang, Xiaoda Zhang   +3 more
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The Novel Successive Variational Mode Decomposition and Weighted Regularized Extreme Learning Machine for Fault Diagnosis of Automobile Gearbox

Shock and Vibration, 2021
In order to improve the diagnosis accuracies of the current diagnosis methods, a novel fault diagnosis method of automobile gearbox based on novel successive variational mode decomposition and weighted regularized extreme learning machine is presented ...
Yijiao Wang, Guoguang Zhou
doaj   +1 more source

Composite fault diagnosis of gearbox based on empirical mode decomposition and improved variational mode decomposition

Journal of Low Frequency Noise, Vibration and Active Control, 2021
In order to identify the nonlinear nonstationary pitting-wear fault signal of gears in gearbox, a new method of composite fault diagnosis for gearbox is proposed, which combines empirical mode decomposition with improved variational mode decomposition ...
Jingyue Wang, Jiangang Li, Haotian Wang, Lixin Guo   +3 more
doaj   +1 more source

Adaptive Complex Variational Mode Decomposition for Micro-Motion Signal Processing Applications

Sensors, 2021
In order to suppress the strong clutter component and separate the effective fretting component from narrow-band radar echo, a method based on complex variational mode decomposition (CVMD) is proposed in this paper.
Saiqiang Xia, Jun Yang, Wanyong Cai, Chaowei Zhang, Liangfa Hua, Zibo Zhou   +5 more
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Application of a flat variational modal decomposition algorithm in fault diagnosis of rolling bearings

Journal of Low Frequency Noise, Vibration and Active Control, 2020
Fault diagnosis of rolling bearings can effectively prevent sudden accidents and is an important factor for the safe operation of mechanical systems.
Haodong Li, Ying Xu, Dong An, Lixiu Zhang, Songhua Li, Huaitao Shi   +5 more
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Reduced-order variational mode decomposition

, 2022
A novel data-driven method of modal analysis for complex flow dynamics, termed as reduced-order variational mode decomposition (RVMD), has been proposed, combining the idea of the separation of variables and a state-of-the-art nonstationary signal-processing technique -- variational mode decomposition.
Liao, Zi-Mo, Zhao, Zhiye, Chen, Liang-Bing, Wan, Zhen-Hua, Liu, Nan-Sheng, Lu, Xi-Yun   +5 more
openaire   +2 more sources

Air gap eccentric analysis and fault detection of traction motor

Journal of Engineering and Applied Science, 2023
To solve the problem of air gap eccentric fault of traction motor, the fault characteristic frequency is close to the fundamental frequency, and the decomposed frequency affects each other, which is easy to cause spectrum aliasing.
Jintian Yin, Zhilong He, Li Liu, Zhihua Peng   +3 more
doaj   +1 more source

Enhanced Discrimination of Seismic Geological Channels Based on Multi-Trace Variational Mode Decomposition

Applied Sciences, 2022
The spectral decomposition is a valuable tool for improving the resolution of seismic interpretation, and thus can improve the accuracy of the subtle geo-features (thin and narrow channels, thin reservoirs, etc.).
Jiaxuan Leng, Zhichao Yu, Chaodong Wu
doaj   +1 more source

Fault diagnosis of rolling bearings based on variational mode decomposition and calculus enhanced energy operator

工程科学学报, 2016
Aiming at the characteristics of rolling bearing fault vibration signals and considering the merits of variational mode decomposition in mono-component separation and calculus enhanced energy operator in transient impulse detection, this article ...
ZHANG Dong, FENG Zhi-peng
doaj   +1 more source

Deep Prediction Model Based on Dual Decomposition with Entropy and Frequency Statistics for Nonstationary Time Series

Entropy, 2022
The prediction of time series is of great significance for rational planning and risk prevention. However, time series data in various natural and artificial systems are nonstationary and complex, which makes them difficult to predict.
Zhigang Shi, Yuting Bai, Xuebo Jin, Xiaoyi Wang, Tingli Su, Jianlei Kong   +5 more
doaj   +1 more source