Journal ArticleDOI
Understanding and improving deep learning-based rolling bearing fault diagnosis with attention mechanism
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TLDR
Attention mechanism is introduced to assist the deep network to locate the informative data segments, extract the discriminative features of inputs, and visualize the learned diagnosis knowledge.About:
This article is published in Signal Processing.The article was published on 2019-08-01. It has received 221 citations till now. The article focuses on the topics: Deep learning & Artificial neural network.read more
Citations
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Journal ArticleDOI
Diagnosing Rotating Machines With Weakly Supervised Data Using Deep Transfer Learning
TL;DR: A novel transfer learning method for diagnostics based on deep learning is proposed, where the diagnostic knowledge learned from sufficient supervised data of multiple rotating machines is transferred to the target equipment with domain adversarial training.
Journal ArticleDOI
Deep learning algorithms for rotating machinery intelligent diagnosis: An open source benchmark study.
TL;DR: A comprehensive evaluation of DL-based intelligent diagnosis models with two data split strategies, five input formats, three normalization methods, and four augmentation methods is performed, and a unified code framework for comparing and testing models fairly and quickly is released.
Journal ArticleDOI
Deep learning-based prognostic approach for lithium-ion batteries with adaptive time-series prediction and on-line validation
TL;DR: The experimental results show the prognostic performances are promising both for the multi-steps-ahead predictions and long-horizon SOH estimations.
Journal ArticleDOI
An explainable artificial intelligence approach for unsupervised fault detection and diagnosis in rotating machinery
TL;DR: A new approach for fault detection and diagnosis in rotating machinery is proposed, namely: unsupervised classification and root cause analysis, and a comparison between models used in machine learning explainability: SHAP and Local Depth-based Feature Importance for the Isolation Forest (Local-DIFFI).
Journal ArticleDOI
A hybrid attention improved ResNet based fault diagnosis method of wind turbines gearbox
TL;DR: A hybrid attention improved residual network (HA-ResNet) based method is proposed to diagnose the fault of wind turbines gearbox by highlighting the essential frequency bands of wavelet coefficients and the fault features of convolution channels.
References
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Long short-term memory
TL;DR: A novel, efficient, gradient based method called long short-term memory (LSTM) is introduced, which can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units.
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Learning representations by back-propagating errors
TL;DR: Back-propagation repeatedly adjusts the weights of the connections in the network so as to minimize a measure of the difference between the actual output vector of the net and the desired output vector, which helps to represent important features of the task domain.
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Reducing the Dimensionality of Data with Neural Networks
TL;DR: In this article, an effective way of initializing the weights that allows deep autoencoder networks to learn low-dimensional codes that work much better than principal components analysis as a tool to reduce the dimensionality of data is described.
Proceedings Article
Understanding the difficulty of training deep feedforward neural networks
Xavier Glorot,Yoshua Bengio +1 more
TL;DR: The objective here is to understand better why standard gradient descent from random initialization is doing so poorly with deep neural networks, to better understand these recent relative successes and help design better algorithms in the future.
Posted Content
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
Kelvin Xu,Jimmy Ba,Ryan Kiros,Kyunghyun Cho,Aaron Courville,Ruslan Salakhutdinov,Richard S. Zemel,Yoshua Bengio +7 more
TL;DR: This paper proposed an attention-based model that automatically learns to describe the content of images by focusing on salient objects while generating corresponding words in the output sequence, which achieved state-of-the-art performance on three benchmark datasets: Flickr8k, Flickr30k and MS COCO.
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