Journal Article10.1016/J.YMSSP.2017.11.016
Machinery health prognostics: A systematic review from data acquisition to RUL prediction
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TL;DR: A review on machinery prognostics following its whole program, i.e., from data acquisition to RUL prediction, which provides discussions on current situation, upcoming challenges as well as possible future trends for researchers in this field.
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About: This article is published in Mechanical Systems and Signal Processing. The article was published on 01 May 2018. The article focuses on the topics: Prognostics.
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Citations
A Novel Scheme for Accurate Remaining Useful Life Prediction for Industrial IoTs by Using Deep Neural Network
TL;DR: A deep learning-based method by combining CNN (Convolutional Neural Networks) and LSTM (Long Short-Term Memory)neural networks to predict RUL for industrial equipment outperforms standard well-accepted machine learning algorithms and accomplishes competitive performance when compared to the state-of-the art methods.
Deep Transfer Learning Remaining Useful Life Prediction of Different Bearings
Juan Xu,Mengting Fang,Weihua Zhao,Yuqi Fan,Xu Ding +4 more
- 18 Jul 2021
TL;DR: Wang et al. as discussed by the authors proposed a new deep transfer learning-based RUL prediction method (DTL-RULPM) which adopted min-max normalization to normalize the original vibration data of bearing.
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Degradation Indicator Construction Using Dual-Class Component Feature Fusion Recalibration for Bearing Performance Evaluation
Yuanyuan Zhou,Hang Wang,Yongbin Liu,Xianzeng Liu,Zheng Cao,Yangyang Fu +5 more
TL;DR: A novel DI is constructed for evaluating the performance degradation of bearing using dual-class component feature fusion recalibration (CFFR) and results indicate that the proposed DI could improve the prediction performance.
4
Transformer-Based Novel Framework for Remaining Useful Life Prediction of Lubricant in Operational Rolling Bearings
Sung-Hyun Kim,Yun-Ho Seo,Junhong Park +2 more
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•Posted Content
Uncovering the Underlying Physics of Degrading System Behavior Through a Deep Neural Network Framework: The Case of Remaining Useful Life Prognosis
TL;DR: An open-box approach using a deep neural network framework to explore the physics of degradation through partial differential equations (PDEs) and aims to discover a latent variable and corresponding PDE to represent the health state of the system.
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