Author
Sheng-Fa Yuan
Other affiliations: Jiangxi University of Science and Technology
Bio: Sheng-Fa Yuan is an academic researcher from Tsinghua University. The author has contributed to research in topics: Support vector machine & Ranking SVM. The author has an hindex of 3, co-authored 3 publications receiving 389 citations. Previous affiliations of Sheng-Fa Yuan include Jiangxi University of Science and Technology.
Papers
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TL;DR: A new multi-class classification of SVM named ‘one to others’ algorithm is presented to solve the multi- class recognition problems and the effectiveness of the method is verified by the application to the fault diagnosis for turbo pump rotor.
191 citations
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TL;DR: A new method that jointly optimises the feature selection and the SVM parameters with a modified discrete particle swarm optimisation and has fewer errors and a better real-time capacity than the method based on principal component analysis (PCA) and SVM.
131 citations
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TL;DR: In this paper, an Artificial Immunization algorithm (AIA) is used to optimise the parameters in SVM in order to avoid the premature convergence and guarantee the variety of solution, the AIA is a new optimisation method based on the biologic immune principle of human being and other living beings.
85 citations
Cited by
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TL;DR: This paper presents a survey of machine condition monitoring and fault diagnosis using support vector machine (SVM), and attempts to summarize and review the recent research and developments of SVM in machine condition Monitoring and diagnosis.
1,228 citations
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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.
1,173 citations
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TL;DR: The results show that the machine learning algorithms can be used for automated diagnosis of bearing faults and it is observed that the severe (chaotic) vibrations occur under bearings with rough inner race surface and ball with corrosion pitting.
Abstract: Ball bearings faults are one of the main causes of breakdown of rotating machines. Thus, detection and diagnosis of mechanical faults in ball bearings is very crucial for the reliable operation. This study is focused on fault diagnosis of ball bearings using artificial neural network (ANN) and support vector machine (SVM). A test rig of high speed rotor supported on rolling bearings is used. The vibration response are obtained and analyzed for the various defects of ball bearings. The specific defects are considered as crack in outer race, inner race with rough surface and corrosion pitting in balls. Statistical methods are used to extract features and to reduce the dimensionality of original vibration features. A comparative experimental study of the effectiveness of ANN and SVM is carried out. The results show that the machine learning algorithms mentioned above can be used for automated diagnosis of bearing faults. It is also observed that the severe (chaotic) vibrations occur under bearings with rough inner race surface and ball with corrosion pitting.
363 citations
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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.
316 citations
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TL;DR: The current paper is intended as a tutorial overview of the basic theory of some of the most common methods of natural computing as they are applied in the context of mechanical systems research.
257 citations