Journal ArticleDOI
Multi-bearing remaining useful life collaborative prediction: A deep learning approach
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TLDR
An integrated deep learning approach for multi-bearing remaining useful life collaborative prediction by combining both time domain features and frequency domain features is proposed, which can extract high-quality degradation patterns of rolling bearing from vibration signals.About:
This article is published in Journal of Manufacturing Systems.The article was published on 2017-04-01. It has received 197 citations till now. The article focuses on the topics: Deep learning & Artificial neural network.read more
Citations
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Journal ArticleDOI
Remaining useful life estimation in prognostics using deep convolution neural networks
Xiang Li,Qian Ding,Jian-Qiao Sun +2 more
TL;DR: A new data-driven approach for prognostics using deep convolution neural networks (DCNN) using time window approach is employed for sample preparation in order for better feature extraction by DCNN.
Journal ArticleDOI
Deep learning-based remaining useful life estimation of bearings using multi-scale feature extraction
TL;DR: A novel intelligent remaining useful life (RUL) prediction method based on deep learning is proposed, and high accuracy on the RUL prediction is achieved, and the proposed method is promising for industrial applications.
Journal ArticleDOI
Rolling bearing fault feature learning using improved convolutional deep belief network with compressed sensing
TL;DR: A novel method called improved convolutional deep belief network (CDBN) with compressed sensing (CS) is developed for feature learning and fault diagnosis of rolling bearing and results confirm that the developed method is more effective than the traditional methods.
Journal ArticleDOI
Transfer Learning With Neural Networks for Bearing Fault Diagnosis in Changing Working Conditions
TL;DR: Experimental results show that the proposed transfer learning approach for fault diagnosis with neural networks can improve the classification accuracy and reduce the training time comparing with the conventional neural network method when there are only a small amount of target data.
Journal ArticleDOI
Bearing remaining useful life prediction based on deep autoencoder and deep neural networks
TL;DR: A novel eigenvector based on time–frequency-wavelet joint features is proposed to effectively represent bearing degradation process and a deep autoencoder based joint features compression and computing method is presented to retain effective information without increasing the scale of DNN.
References
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Journal ArticleDOI
Deep learning in neural networks
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A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking
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On the importance of initialization and momentum in deep learning
TL;DR: It is shown that when stochastic gradient descent with momentum uses a well-designed random initialization and a particular type of slowly increasing schedule for the momentum parameter, it can train both DNNs and RNNs to levels of performance that were previously achievable only with Hessian-Free optimization.
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