Journal ArticleDOI
Fault diagnosis of rotating machinery based on improved wavelet package transform and SVMs ensemble
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TLDR
The proposed method is applied to the fault diagnosis of rolling element bearings, and testing results show that the SVMs ensemble can reliably separate different fault conditions and identify the severity of incipient faults, which has a better classification performance compared to the single SVMs.About:
This article is published in Mechanical Systems and Signal Processing.The article was published on 2007-02-01. It has received 371 citations till now. The article focuses on the topics: Biorthogonal wavelet & Wavelet.read more
Citations
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Journal ArticleDOI
Support vector machine in machine condition monitoring and fault diagnosis
Achmad Widodo,Bo-Suk Yang +1 more
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.
Journal ArticleDOI
Wavelets for fault diagnosis of rotary machines: A review with applications
TL;DR: Current applications of wavelets in rotary machine fault diagnosis are summarized and some new research trends, including wavelet finite element method, dual-tree complex wavelet transform, wavelet function selection, newWavelet function design, and multi-wavelets that advance the development of wavelet-based fault diagnosed are discussed.
Journal ArticleDOI
Construction of hierarchical diagnosis network based on deep learning and its application in the fault pattern recognition of rolling element bearings
Meng Gan,Cong Wang,Chang׳an Zhu +2 more
TL;DR: The experimental results show that HDN is highly reliable for precise multi-stage diagnosis and can overcome the overlapping problem caused by noise and other disturbances.
Journal ArticleDOI
Wavelet transform based on inner product in fault diagnosis of rotating machinery: A review
TL;DR: In this article, the inner product operation of wavelet transform (WT) is verified by simulation and field experiments and the development process of WT based on inner product is concluded and the applications of major developments in rotating machinery fault diagnosis are also summarized.
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.
References
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Journal ArticleDOI
LIBSVM: A library for support vector machines
Chih-Chung Chang,Chih-Jen Lin +1 more
TL;DR: Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
Book
The Nature of Statistical Learning Theory
TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
Book
Principal Component Analysis
TL;DR: In this article, the authors present a graphical representation of data using Principal Component Analysis (PCA) for time series and other non-independent data, as well as a generalization and adaptation of principal component analysis.
Journal ArticleDOI
A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting
Yoav Freund,Robert E. Schapire +1 more
TL;DR: The model studied can be interpreted as a broad, abstract extension of the well-studied on-line prediction model to a general decision-theoretic setting, and it is shown that the multiplicative weight-update Littlestone?Warmuth rule can be adapted to this model, yielding bounds that are slightly weaker in some cases, but applicable to a considerably more general class of learning problems.
Reference EntryDOI
Principal Component Analysis
TL;DR: Principal component analysis (PCA) as discussed by the authors replaces the p original variables by a smaller number, q, of derived variables, the principal components, which are linear combinations of the original variables.
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Support vector machine in machine condition monitoring and fault diagnosis
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