Results 1 to 10 of about 324 (121)

Variable-Step Multiscale Fuzzy Dispersion Entropy: A Novel Metric for Signal Analysis [PDF]

Entropy, 2023
Fuzzy dispersion entropy (FuzDE) is a newly proposed entropy metric, which combines the superior characteristics of fuzzy entropy (FE) and dispersion entropy (DE) in signal analysis.
Yuxing Li, Junxian Wu, Shuai Zhang, Bingzhao Tang, Yilan Lou   +4 more
doaj   +6 more sources

Refined Composite Multiscale Fuzzy Dispersion Entropy and Its Applications to Bearing Fault Diagnosis [PDF]

Entropy, 2023
Rotary machines often exhibit nonlinear behavior due to factors such as nonlinear stiffness, damping, friction, coupling effects, and defects. Consequently, their vibration signals display nonlinear characteristics.
Mostafa Rostaghi, Mohammad Mahdi Khatibi, Mohammad Reza Ashory, Hamed Azami   +3 more
doaj   +6 more sources

Parameter Analysis of Multiscale Two-Dimensional Fuzzy and Dispersion Entropy Measures Using Machine Learning Classification [PDF]

Entropy, 2021
Two-dimensional fuzzy entropy, dispersion entropy, and their multiscale extensions (MFuzzyEn2D and MDispEn2D, respectively) have shown promising results for image classifications.
Ryan Furlong, Mirvana Hilal, Vincent O’Brien, Anne Humeau-Heurtier   +3 more
doaj   +5 more sources

Research on Feature Extraction of Ship-Radiated Noise Based on Multiscale Fuzzy Dispersion Entropy

Journal of Marine Science and Engineering, 2023
In recent years, fuzzy dispersion entropy (FDE) has been proposed and used in the feature extraction of various types of signals. However, FDE can only analyze a signal from a single time scale during practical application and ignores some important ...
Yuxing Li, Yilan Lou, Lili Liang, Shuai Zhang   +3 more
doaj   +4 more sources

Quantifying Heart Rate Variability Using Multiscale Fuzzy Dispersion Entropy

IEEE Access
Heart rate variability (HRV), which is the variation of inter-beat intervals, exhibits complex characteristics on multiple temporal scales due to the balancing function of the autonomic nervous system.
Chae-Min Kim, Young-Seok Choi
doaj   +4 more sources

Intelligent Fault Diagnosis of Rolling Bearings Based on a Complete Frequency Range Feature Extraction and Combined Feature Selection Methodology [PDF]

Sensors, 2023
The utilization of multiscale entropy methods to characterize vibration signals has proven to be promising in intelligent diagnosis of mechanical equipment.
Zhengkun Xue, Yukun Huang, Wanyang Zhang, Jinchuan Shi, Huageng Luo   +4 more
doaj   +2 more sources

Bearing Fault Diagnosis Using Refined Composite Generalized Multiscale Dispersion Entropy-Based Skewness and Variance and Multiclass FCM-ANFIS

Entropy, 2021
Bearing vibration signals typically have nonlinear components due to their interaction and coupling effects, friction, damping, and nonlinear stiffness. Bearing faults affect the signal complexity at various scales.
Mostafa Rostaghi, Mohammad Mahdi Khatibi, Mohammad Reza Ashory, Hamed Azami   +3 more
doaj   +3 more sources

Refined Composite Multivariate Multiscale Dispersion Entropy and Its Application to Fault Diagnosis of Rolling Bearing

IEEE Access, 2019
Many nonlinear dynamic and statistic methods, including multiscale sample entropy (MSE) and multiscale fuzzy entropy (MFE), have been widely studied and employed to fault diagnosis of the rolling bearing. Multiscale dispersion entropy (MDE) is a powerful
Congzhi Li, Jinde Zheng, Haiyang Pan, Jinyu Tong, Yifang Zhang   +4 more
doaj   +3 more sources

A Novel Method for Fault Diagnosis of Rotating Machinery [PDF]

Entropy, 2022
When rotating machinery fails, the consequent vibration signal contains rich fault feature information. However, the vibration signal bears the characteristics of nonlinearity and nonstationarity, and is easily disturbed by noise, thus it may be ...
Meng Tang, Yaxuan Liao, Fan Luo, Xiangshun Li   +3 more
doaj   +2 more sources

Application of FCEEMD-TSMFDE and adaptive CatBoost in fault diagnosis of complex variable condition bearings [PDF]

Scientific Reports
The mode mixing problem and inherent mode function selection bias in Fast Ensemble Empirical Mode Decomposition (FEEMD) result in ineffective extraction of fault components during the denoising stage, the loss of coarse-grained information in Multiscale ...
Min Mao, Bingwei Xu, Yuhuan Sun, Kairong Tan, Yuran Wang, Chao Zhou, Chengjiang Zhou, Jingzong Yang   +7 more
doaj   +2 more sources