Results 31 to 40 of about 7,852 (282)

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
doaj   +1 more source

Empirical Variational Mode Decomposition Based on Binary Tree Algorithm. [PDF]

Sensors (Basel), 2022
Aiming at non-stationary signals with complex components, the performance of a variational mode decomposition (VMD) algorithm is seriously affected by the key parameters such as the number of modes [Formula: see text] , the quadratic penalty parameter ...
Li H, Xu B, Zhou F, Yan B, Zhou F.
europepmc   +2 more sources

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
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

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

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

ROTOR FAULT FEATURE EXTRACTION METHOD USING MVMD AND IEDPE (MT)

Jixie qiangdu, 2023
To accurately characterize the different operating states of the rotor system, a modified variational mode decomposition（MVMD） combined with instantaneous energy distribution permutation entropy（IEDPE） is proposed to quantify and extract rotor fault ...
WU YaoChun, GUO XiaoBo, DU ShaoHua, JU Hai, ZHAO Yan, WU Jie, JIA XiaoFeng   +6 more
doaj  

A Novel Intelligent Fault Diagnosis Method Based on Variational Mode Decomposition and Ensemble Deep Belief Network

IEEE Access, 2020
The deep belief network is widely used in fault diagnosis and health management of rotating machinery. However, on the one hand, deep belief networks only tend to focus on the global information of bearing vibration, ignoring local information.
Chao Zhang, Yibin Zhang, Chenxi Hu, Zhenbao Liu, Liye Cheng, Yong Zhou   +5 more
doaj   +1 more source

Identification of normal and depression EEG signals in variational mode decomposition domain

, 2022
Early detection of depression is critical in assisting patients in receiving the best therapy possible to avoid negative repercussions. Depression detection using electroencephalogram (EEG) signals is a simple, low-cost, convenient, and accurate approach.
Akbari, Hesam, Li, Yan, Sadiq, Muhammad Tariq, Siuly, Siuly, Sadiq, Muhammad Tariq; id_orcid, Wen, Paul   +5 more
core   +1 more source

Multichannel Signal Denoising Using Multivariate Variational Mode Decomposition With Subspace Projection

IEEE Access, 2020
This paper describes a novel multichannel signal denoising approach based on multivariate variational mode decomposition (MVMD). MVMD is the extended version of the variational mode decomposition (VMD) algorithm for multichannel data sets.
Peipei Cao, Huali Wang, Kaijie Zhou
doaj   +1 more source