Journal ArticleDOI
A novel gas turbine fault diagnosis method based on transfer learning with CNN
Shi-sheng Zhong,Song Fu,Lin Lin +2 more
TLDR
It is shown how feature representations learned with CNN on large-scale annotated gas turbine normal dataset can be efficiently transferred to fault diagnosis task with limited fault data.About:
This article is published in Measurement.The article was published on 2019-04-01. It has received 152 citations till now. The article focuses on the topics: Feature (computer vision) & Fault (power engineering).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
Deep transfer multi-wavelet auto-encoder for intelligent fault diagnosis of gearbox with few target training samples
TL;DR: A novel approach named deep transfer multi-wavelet auto-encoder is presented for gearbox intelligent fault diagnosis with few training samples and transfer diagnosis cases for different fault severities and compound faults of gearbox confirm the feasibility of the proposed approach.
Journal ArticleDOI
An adaptive deep transfer learning method for bearing fault diagnosis
TL;DR: An adaptive deep transfer learning method for bearing fault diagnosis is proposed, a long-short term memory recurrent neural network model based on instance-transfer learning is constructed, and grey wolf optimization algorithm is introduced to adaptively learn key parameters of joint distribution adaptation.
Journal ArticleDOI
Ensemble transfer CNNs driven by multi-channel signals for fault diagnosis of rotating machinery cross working conditions
TL;DR: A new decision fusion strategy is designed to flexibly fuse each individual target CNN to obtain the comprehensive result of the proposed ensemble transfer convolutional neural networks driven by multi-channel signals.
Journal ArticleDOI
Applications of Unsupervised Deep Transfer Learning to Intelligent Fault Diagnosis: A Survey and Comparative Study
TL;DR: Zhao et al. as mentioned in this paper constructed a taxonomy and performed a comprehensive review of unsupervised deep transfer learning (UDTL)-based intelligent fault diagnosis (IFD) according to different tasks.
References
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Proceedings ArticleDOI
Deep Residual Learning for Image Recognition
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Journal ArticleDOI
Deep learning
TL;DR: Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.
Book
Deep Learning
TL;DR: Deep learning as mentioned in this paper is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts, and it is used in many applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames.
Journal ArticleDOI
A Survey on Transfer Learning
Sinno Jialin Pan,Qiang Yang +1 more
TL;DR: The relationship between transfer learning and other related machine learning techniques such as domain adaptation, multitask learning and sample selection bias, as well as covariate shift are discussed.