Journal Article10.1016/J.YMSSP.2019.106330
Deep separable convolutional network for remaining useful life prediction of machinery
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TL;DR: The experimental results show that the proposed deep separable convolutional network (DSCN) is able to provide accurate RUL prediction results based on the raw multi-sensor data and is superior to some existing data-driven prognostics approaches.
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About: This article is published in Mechanical Systems and Signal Processing. The article was published on 01 Dec 2019. The article focuses on the topics: Prognostics & Deep learning.
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Citations
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.
A multi-stage semi-supervised learning approach for intelligent fault diagnosis of rolling bearing using data augmentation and metric learning
TL;DR: A three-stage SSL approach using data augmentation (DA) and metric learning is proposed for an intelligent bearing fault diagnosis under limited labeled data to demonstrate that the proposed method can perform better in bearing fault diagnosed under limited labeling samples than existing diagnostic methods.
253
Recurrent convolutional neural network: A new framework for remaining useful life prediction of machinery
TL;DR: Experimental results demonstrate the effectiveness and superiority of RCNN in improving the accuracy and convergence of RUL prediction, and more importantly, RCNN is able to provide a probabilistic RUL Prediction result, which breaks the inherent limitation of CNNs and facilitates maintenance decision making.
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A novel temporal convolutional network with residual self-attention mechanism for remaining useful life prediction of rolling bearings
TL;DR: A new deep learning framework – Temporal convolutional network with residual self-attention mechanism (TCN-RSA), which can learn both time-frequency and temporal information of signals and outperforms the other state-of-the-art methods in RUL prediction and system prognosis with respect to better accuracy and computation efficiency.
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An improved similarity-based prognostic algorithm for RUL estimation using an RNN autoencoder scheme
TL;DR: An ensemble approach that integrates the Rul estimations obtained from the similarity-based curve matching techniques, with and without the zero-centering rules, is introduced to increase the robustness and accuracy of proposed method for RUL estimations.
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