Composite multi-scale weighted permutation entropy and extreme learning machine based intelligent fault diagnosis for rolling bearing
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Summary
The proposed composite multi-scale weighted permutation entropy methodology is proposed and the results show that CMWPE has less dependence on data length and the estimated entropy values are much more stable than the other existing methods.
- Type
- article
- Published
- 2019-09-01
- Cited by
- 91
- References
- 26
- OpenAlex
- https://openalex.org/W2944095229
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:164848710
Keywords
Granularity, Entropy (arrow of time), Algorithm, Computer science, Permutation (music)
References
- A rolling bearing fault diagnosis approach based on LCD and fuzzy entropy
- Quantitative diagnosis of a spall-like fault of a rolling element bearing by empirical mode decomposition and the approximate entropy method
- Weighted multiscale permutation entropy of financial time series
- Bearing Fault Diagnosis Based on Multiscale Permutation Entropy and Support Vector Machine
- Multifault Diagnosis for Rolling Element Bearings Based on Intrinsic Mode Permutation Entropy and Ensemble Optimal Extreme Learning Machine
- Multiscale entropy analysis of complex physiologic time series.
- Extreme learning machine: Theory and applications
- Extreme Learning Machine for Multilayer Perceptron
- Weighted-permutation entropy: a complexity measure for time series incorporating amplitude information.
- Adaptive parameterless empirical wavelet transform based time-frequency analysis method and its application to rotor rubbing fault diagnosis
- Weighted-Permutation Entropy Analysis of Resting State EEG from Diabetics with Amnestic Mild Cognitive Impairment
- A diagnostic signal selection scheme for planetary gearbox vibration monitoring under non-stationary operational conditions
- GNAR-GARCH model and its application in feature extraction for rolling bearing fault diagnosis
- Intelligent fault diagnosis of rolling bearing using hierarchical convolutional network based health state classification
- Generalized composite multiscale permutation entropy and Laplacian score based rolling bearing fault diagnosis
- A phase angle based diagnostic scheme to planetary gear faults diagnostics under non-stationary operational conditions
- Stacked Sparse Autoencoder-Based Deep Network for Fault Diagnosis of Rotating Machinery
- Intelligent fault diagnosis of rolling bearing using deep wavelet auto-encoder with extreme learning machine
- Fault State Recognition of Rolling Bearing Based Fully Convolutional Network
- Vibration-Based Intelligent Fault Diagnosis for Roller Bearings in Low-Speed Rotating Machinery
Cited by
- An Efficient Porcine Acoustic Signal Denoising Technique Based on EEMD-ICA-WTD
- Uses of empirical mode decomposition and multi-entropy techniques to establish the correlations among vibrations, friction coefficients and component wear of ball-bearing-like specimens
- Fault Diagnosis Method for High-Pressure Common Rail Injector Based on IFOA-VMD and Hierarchical Dispersion Entropy
- Related Entropy Theories Application in Condition Monitoring of Rotating Machineries
- Non-parallel least squares support matrix machine for rolling bearing fault diagnosis
- Classification of hand movements using variational mode decomposition and composite permutation entropy index with surface electromyogram signals
- Fault Diagnosis for Rolling Bearings Based on Composite Multiscale Fine-Sorted Dispersion Entropy and SVM With Hybrid Mutation SCA-HHO Algorithm Optimization
- Remaining useful life prediction of rolling bearing using fractal theory
- An enhanced convolutional neural network for bearing fault diagnosis based on time–frequency image
- Data synthesis using dual discriminator conditional generative adversarial networks for imbalanced fault diagnosis of rolling bearings
- Health condition identification for rolling bearing based on hierarchical multiscale symbolic dynamic entropy and least squares support tensor machine–based binary tree
- Fault detection of rolling bearing based on principal component analysis and empirical mode decomposition
- Bearing fault detection and recognition methodology based on weighted multiscale entropy approach
- A tool condition monitoring method based on two-layer angle kernel extreme learning machine and binary differential evolution for milling
- Use of generalized refined composite multiscale fractional dispersion entropy to diagnose the faults of rolling bearing
- Rolling Bearing Diagnosis Based on Adaptive Probabilistic PCA and the Enhanced Morphological Filter
- A novel transfer learning fault diagnosis method based on Manifold Embedded Distribution Alignment with a little labeled data
- Discriminative manifold random vector functional link neural network for rolling bearing fault diagnosis
- A comprehensive evaluation of the effect of defect size in rolling element bearings on the statistical features of the vibration signal
- A Fault Classification Method for Rolling Bearing Based on Multisynchrosqueezing Transform and WOA-SMM
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