Journal ArticleDOI
Multi-fault diagnosis study on roller bearing based on multi-kernel support vector machine with chaotic particle swarm optimization
TLDR
The experimental results indicate that this proposed approach is an effective method for roller bearing fault diagnosis, which has more strong generalization ability and can achieve higher diagnostic accuracy than that of the single kernel SVM or the MSVM which parameters are randomly extracted.About:
This article is published in Measurement.The article was published on 2014-01-01. It has received 106 citations till now. The article focuses on the topics: Particle swarm optimization & Support vector machine.read more
Citations
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Journal ArticleDOI
Applications of machine learning to machine fault diagnosis: A review and roadmap
TL;DR: A review and roadmap to systematically cover the development of IFD following the progress of machine learning theories and offer a future perspective is presented.
Journal ArticleDOI
A novel bearing fault diagnosis model integrated permutation entropy, ensemble empirical mode decomposition and optimized SVM
TL;DR: In this paper, a hybrid model for fault detection and classification of motor bearing is presented, where the permutation entropy (PE) of the vibration signal is calculated to detect the malfunctions of the bearing.
Journal ArticleDOI
A novel intelligent diagnosis method using optimal LS-SVM with improved PSO algorithm
TL;DR: The fuzzy information entropy can accurately and more completely extract the characteristics of the vibration signal, the improved PSO algorithm can effectively improve the classification accuracy of LS-SVM, and the proposed fault diagnosis method outperforms the other mentioned methods.
Journal ArticleDOI
Intelligent fault diagnosis of roller bearings with multivariable ensemble-based incremental support vector machine
TL;DR: A novel intelligent fault diagnosis method with multivariable ensemble-based incremental support vector machine (MEISVM) is proposed, which proves the capability of detecting multiple faults including complex compound faults and different severe degrees with the same fault.
Journal ArticleDOI
Intelligent fault diagnosis of machines with small & imbalanced data: A state-of-the-art review and possible extensions.
Tianci Zhang,None Fitri S. Kasim,Jinglong Chen,Fudong Li,Kaiyu Zhang,Haixin Lv,Shuilong He,Enyong Xu +7 more
TL;DR: In this paper, a review of the research results on intelligent fault diagnosis with small and imbalanced data (S&I-IFD) is presented, which refers to build intelligent diagnosis models using limited machine faulty samples to achieve accurate fault identification.
References
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Statistical learning theory
TL;DR: Presenting a method for determining the necessary and sufficient conditions for consistency of learning process, the author covers function estimates from small data pools, applying these estimations to real-life problems, and much more.
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The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis
Norden E. Huang,Zheng Shen,Steven R. Long,Man-Li C. Wu,Hsing H. Shih,Quanan Zheng,Nai-Chyuan Yen,C. C. Tung,Henry H. Liu +8 more
TL;DR: In this paper, a new method for analysing nonlinear and nonstationary data has been developed, which is the key part of the method is the empirical mode decomposition method with which any complicated data set can be decoded.
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Particle swarm optimization
TL;DR: A snapshot of particle swarming from the authors’ perspective, including variations in the algorithm, current and ongoing research, applications and open problems, is included.
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Chaotic particle swarm optimization algorithm in a support vector regression electric load forecasting model
TL;DR: This investigation elucidates the feasibility of applying chaotic particle swarm optimization (CPSO) algorithm to choose the suitable parameter combination for a SVR model and outperforms the other two models applying other algorithms, genetic algorithm (GA) and simulated annealing algorithm (SA).
Journal ArticleDOI
Chaotic particle swarm optimization for economic dispatch considering the generator constraints
TL;DR: Two CPSO methods based on the logistic equation and the Tent equation are presented to solve economic dispatch (ED) problems with generator constraints and applied in two power system cases.