Journal ArticleDOI
Rolling bearing fault diagnosis using generalized refined composite multiscale sample entropy and optimized support vector machine
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TLDR
A fault diagnosis for rolling bearings, based on Generalized Refined Composite Multiscale Sample Entropy, Supervised Isometric Mapping, and Grasshopper Optimization Algorithm based Support Vector Machine, which improves the classification accuracy to 100%.About:
This article is published in Measurement.The article was published on 2020-05-01. It has received 124 citations till now. The article focuses on the topics: Support vector machine & Bearing (mechanical).read more
Citations
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Journal ArticleDOI
Modified multiscale weighted permutation entropy and optimized support vector machine method for rolling bearing fault diagnosis with complex signals.
TL;DR: In this article, a fault diagnosis method based on generalized composite multiscale weighted permutation entropy (GCMWPE), supervised Isomap (S-Iso), and marine predators algorithm-based support vector machine (MPA-SVM) was proposed.
Journal ArticleDOI
Imbalanced fault diagnosis of rolling bearing using improved MsR-GAN and feature enhancement-driven CapsNet
TL;DR: In this article , an imbalanced fault diagnosis approach based on improved multi-scale residual generative adversarial network (GAN) and feature enhancement-driven capsule network is proposed to solve it.
Journal ArticleDOI
Multiscale Diversity Entropy: A Novel Dynamical Measure for Fault Diagnosis of Rotating Machinery
Xianzhi Wang,Shubin Si,Yongbo Li +2 more
TL;DR: A fault diagnosis scheme based on multiscale diversity entropy (MDE) and extreme learning machine (ELM) and the highest classification accuracy compared with three existing approaches: sample entropy, fuzzy entropy, and permutation entropy is presented.
Journal ArticleDOI
Fault diagnosis of rolling bearing using marine predators algorithm-based support vector machine and topology learning and out-of-sample embedding
TL;DR: A rolling bearing fault diagnosis method based on refined composite multiscale fuzzy entropy (RCMFE), topology learning and out-of-sample embedding (TLOE), and the marine predators algorithm based-support vector machine (MPA-SVM) is proposed.
Journal ArticleDOI
Fault diagnosis of multi-channel data by the CNN with the multilinear principal component analysis
TL;DR: The tensor structure and characteristics of a multi-channel dataset are investigated and a novel fault diagnosis method is proposed by introducing the multilinear subspace learning algorithm into deep learning technologies.
References
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Journal ArticleDOI
Multiscale entropy analysis of complex physiologic time series.
TL;DR: A method to calculate multiscale entropy (MSE) for complex time series is introduced and it is found that MSE robustly separates healthy and pathologic groups and consistently yields higher values for simulated long-range correlated noise compared to uncorrelated noise.
Journal ArticleDOI
Grasshopper Optimisation Algorithm
TL;DR: The proposed grasshopper optimisation algorithm is able to provide superior results compared to well-known and recent algorithms in the literature and the results of the real applications prove the merits of GOA in solving real problems with unknown search spaces.
Journal ArticleDOI
Supervised nonlinear dimensionality reduction for visualization and classification
TL;DR: The results reveal that S-Isomap excels compared to Isomap and WeightedIso in classification, and it is highly competitive with those well-known classification methods.
Journal ArticleDOI
A novel fault diagnosis technique for photovoltaic systems based on artificial neural networks
TL;DR: In this article, a fault diagnostic technique for photovoltaic systems based on Artificial Neural Networks (ANN) is proposed for a given set of working conditions -i.e., solar irradiance and PV module's temperature -a number of attributes such as current, voltage, and number of peaks in the current voltage characteristics of the PV strings are calculated using a simulation model.
Journal ArticleDOI
A novel optimized SVM classification algorithm with multi-domain feature and its application to fault diagnosis of rolling bearing
Xiaoan Yan,Minping Jia +1 more
TL;DR: Experimental results show that the proposed fault classification algorithm achieves high diagnosis accuracy for different working conditions of rolling bearing and outperforms some traditional methods both mentioned in this paper and published in other literature.
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