Journal ArticleDOI
Multi-fault diagnosis for rotating machinery based on orthogonal supervised linear local tangent space alignment and least square support vector machine
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TLDR
A multi-fault diagnosis method for rotating machinery based on orthogonal supervised linear local tangent space alignment (OSLLTSA) and least square support vector machine (LS-SVM) that achieves the combination of intrinsic structure information and class label information of dataset but also improves the discrimination of the low-dimensional fault features.About:
This article is published in Neurocomputing.The article was published on 2015-06-01. It has received 107 citations till now. The article focuses on the topics: Local tangent space alignment & Fault (power engineering).read more
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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.
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Deep Convolutional Transfer Learning Network: A New Method for Intelligent Fault Diagnosis of Machines With Unlabeled Data
TL;DR: A new intelligent method named deep convolutional transfer learning network (DCTLN) is proposed, which facilitates the 1-D CNN to learn domain-invariant features by maximizing domain recognition errors and minimizing the probability distribution distance.
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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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A Review of Fault Detection and Diagnosis for the Traction System in High-Speed Trains
Hongtian Chen,Bin Jiang +1 more
TL;DR: A comprehensive review on the fault detection and diagnosis techniques for high-speed trains is presented using data-driven methods which are receiving increasing attention in transportation fields over the past ten years.
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Multimodal deep support vector classification with homologous features and its application to gearbox fault diagnosis
TL;DR: This paper addresses a multimodal deep support vector classification (MDSVC) approach, which employs separation-fusion based deep learning in order to perform fault diagnosis tasks for gearboxes, and shows that the proposed model achieves the best fault classification rate in experiments when compared to representative deep and shallow learning methods.
References
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Journal ArticleDOI
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.
Journal ArticleDOI
Nonlinear dimensionality reduction by locally linear embedding.
Sam T. Roweis,Lawrence K. Saul +1 more
TL;DR: Locally linear embedding (LLE) is introduced, an unsupervised learning algorithm that computes low-dimensional, neighborhood-preserving embeddings of high-dimensional inputs that learns the global structure of nonlinear manifolds.
Journal ArticleDOI
A global geometric framework for nonlinear dimensionality reduction.
TL;DR: An approach to solving dimensionality reduction problems that uses easily measured local metric information to learn the underlying global geometry of a data set and efficiently computes a globally optimal solution, and is guaranteed to converge asymptotically to the true structure.
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Eigenfaces vs. Fisherfaces: recognition using class specific linear projection
TL;DR: A face recognition algorithm which is insensitive to large variation in lighting direction and facial expression is developed, based on Fisher's linear discriminant and produces well separated classes in a low-dimensional subspace, even under severe variations in lighting and facial expressions.
Journal ArticleDOI
Independent component analysis: algorithms and applications
Aapo Hyvärinen,Erkki Oja +1 more
TL;DR: The basic theory and applications of ICA are presented, and the goal is to find a linear representation of non-Gaussian data so that the components are statistically independent, or as independent as possible.