Journal ArticleDOI
Rolling element bearing fault diagnosis using convolutional neural network and vibration image
Duy-Tang Hoang,Hee-Jun Kang +1 more
TLDR
This paper proposes a method for diagnosing bearing faults based on a deep structure of convolutional neural network which does not require any feature extraction techniques and achieves very high accuracy and robustness under noisy environments.About:
This article is published in Cognitive Systems Research.The article was published on 2019-01-01. It has received 281 citations till now. The article focuses on the topics: Deep learning & Feature extraction.read more
Citations
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Journal ArticleDOI
A comprehensive review on convolutional neural network in machine fault diagnosis
TL;DR: This work attempts to review and summarize the development of the Convolutional Network based Fault Diagnosis (CNFD) approaches comprehensively, and points out the characteristics of current development, facing challenges and future trends.
Journal ArticleDOI
Bearing Fault Detection and Diagnosis Using Case Western Reserve University Dataset With Deep Learning Approaches: A Review
Dhiraj Neupane,Jongwon Seok +1 more
TL;DR: This paper summarizes the recent works which use the CWRU bearing dataset in machinery fault detection and diagnosis employing deep learning algorithms and can be of good help for future researchers to start their work on machinery fault Detection and diagnosis using the C WRU dataset.
Journal ArticleDOI
A New Intelligent Bearing Fault Diagnosis Method Using SDP Representation and SE-CNN
TL;DR: A new and intelligent bearing fault diagnostic method by combining symmetrized dot pattern (SDP) representation with squeeze-and-excitation-enabled convolutional neural network (SE-CNN) model that achieves the classification rate over 99% but also has better generalization ability and stability.
Journal ArticleDOI
Deep Learning-Based Intelligent Fault Diagnosis Methods Toward Rotating Machinery
TL;DR: DL and DL-based intelligent fault diagnosis techniques for rotating machinery, primarily including bearing, gear/gearbox and pumps, are overviewed and discussed.
Journal ArticleDOI
Deep learning for prognostics and health management: State of the art, challenges, and opportunities
TL;DR: A systematic review of state-of-the-art deep learning-based PHM frameworks emphasizes on the most recent trends within the field and presents the benefits and potentials of state of theart deep neural networks for system health management.
References
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Proceedings ArticleDOI
Going deeper with convolutions
Christian Szegedy,Wei Liu,Yangqing Jia,Pierre Sermanet,Scott Reed,Dragomir Anguelov,Dumitru Erhan,Vincent Vanhoucke,Andrew Rabinovich +8 more
TL;DR: Inception as mentioned in this paper is a deep convolutional neural network architecture that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14).
Journal ArticleDOI
An introduction to variable and feature selection
Isabelle Guyon,André Elisseeff +1 more
TL;DR: The contributions of this special issue cover a wide range of aspects of variable selection: providing a better definition of the objective function, feature construction, feature ranking, multivariate feature selection, efficient search methods, and feature validity assessment methods.
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.
Journal ArticleDOI
Condition Monitoring and Fault Diagnosis of Electrical Motors—A Review
TL;DR: A review paper describing different types of faults and the signatures they generate and their diagnostics' schemes will not be entirely out of place to avoid repetition of past work and gives a bird's eye view to a new researcher in this area.
Journal ArticleDOI
Feature subset selection using a genetic algorithm
J. Yang,Vasant Honavar +1 more
TL;DR: The authors' approach uses a genetic algorithm to select subsets of attributes or features to represent the patterns to be classified, achieving multicriteria optimization in terms of generalization accuracy and costs associated with the features.