Journal Article10.1016/J.MEASUREMENT.2021.109166
Remaining useful life prediction of roller bearings based on improved 1D-CNN and simple recurrent unit
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TL;DR: Results revealed that on the premise of ensuring prediction accuracy, the 1D-CNN-SRU method could reduce manual intervention and time cost to a certain extent and provide an intelligent method for roller bearing remaining useful life prediction.
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About: This article is published in Measurement. The article was published on 01 Apr 2021.
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Citations
Streamflow prediction using an integrated methodology based on convolutional neural network and long short-term memory networks.
Sujan Ghimire,Zaher Mundher Yaseen,Zaher Mundher Yaseen,Aitazaz A. Farooque,Ravinesh C. Deo,Ji Zhang,Xiaohui Tao +6 more
TL;DR: In this article, a CNN-LSTM model was proposed to predict the hourly streamflow at Brisbane River and Teewah Creek, Australia. And the proposed model outperformed all the benchmarked conventional AI models as well as ensemble models for all the time intervals.
Deep Transfer Learning Based on Bi-LSTM and Attention for Remaining Useful Life Prediction of Rolling Bearing
TL;DR: In this paper , a failure behavior judgment method is proposed by using the convolutional autoencoder (CAE) and Pearson correlation coefficient to determine whether the bearing fails gradually or suddenly, and a multi-channel transfer network is proposed for extracting multi-scale features of bearing degradation.
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Remaining useful life prediction of bearing based on stacked autoencoder and recurrent neural network
TL;DR: Wang et al. as mentioned in this paper used the bottleneck structure of Stacked Autoencoder (SAE) to fuse the four selected features into one health indication (HI) using Intelligent Maintenance Systems (IMS) bearing dataset as training sample.
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Data-driven bearing health management using a novel multi-scale fused feature and gated recurrent unit
Qing Ni,Junjie Ji,Ke Feng,Yongchao Zhang,Dongdong Lin,Jinde Zheng +5 more
- 01 Oct 2023
TL;DR: This paper proposes a novel data-driven approach for bearing health management, using a multi-scale fused feature and gated recurrent unit network to predict remaining useful life with high accuracy and generalizability, alleviating random fluctuations and degradation characteristics.
80
A Novel Deep Learning Model Integrating CNN and GRU to Predict Particulate Matter Concentrations
TL;DR: In this paper , a deep learning model called RF-CNN-GRU, which combines random forest (RF), convolutional neural network (CNN) and gated recurrent unit (GRU), is proposed to predict atmospheric PM2.5 concentrations with incomplete original data.
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A Hybrid Prognostics Approach for Estimating Remaining Useful Life of Rolling Element Bearings
TL;DR: Experimental results demonstrate the effectiveness of the proposed hybrid prognostics approach in improving the accuracy and convergence of RUL prediction of rolling element bearings.
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•Posted Content
1D Convolutional Neural Networks and Applications: A Survey
TL;DR: A comprehensive review of the general architecture and principals of 1D CNNs along with their major engineering applications, especially focused on the recent progress in this field, is presented in this paper, where the benchmark datasets and the principal 1D convolutional neural network software used in those applications are also publically shared in a dedicated website.