Journal ArticleDOI
Recent advances and trends of predictive maintenance from data-driven machine prognostics perspective
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TLDR
In this article , a comprehensive review of recent advances and trends of data-driven machine prognostics, with a focus on their applications in practice, is presented, and a discussion on the challenges, opportunities, and future trends of predictive maintenance is presented.About:
This article is published in Measurement.The article was published on 2022-01-01. It has received 35 citations till now. The article focuses on the topics: Prognostics & Predictive maintenance.read more
Citations
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Tool wear prediction method based on symmetrized dot pattern and multi-covariance Gaussian process regression
TL;DR: Wang et al. as discussed by the authors proposed a wear stage division-based tool wear prediction method based on the improved symmetrized dot pattern (ISDP) and multi-covariance Gaussian process regression (MCGPR).
Journal ArticleDOI
Tool wear prediction method based on symmetrized dot pattern and multi-covariance Gaussian process regression
TL;DR: Wang et al. as discussed by the authors proposed a wear stage division-based tool wear prediction method based on the improved symmetrized dot pattern (ISDP) and multi-covariance Gaussian process regression (MCGPR).
Journal ArticleDOI
Machine Learning and Artificial Intelligence in CNC Machine Tools, A Review
TL;DR: In this paper , the authors present an overview of current research on machine learning and artificial intelligence approaches in CNC machining processes, which can be used in order to enhance almost every technology-enabled service, products and industrial applications.
Journal ArticleDOI
Remaining useful life prediction of rolling bearing based on multi-head attention embedded Bi-LSTM network
TL;DR: Wang et al. as discussed by the authors proposed a novel data-driven method to predict the RUL of rolling bearings using multi-head attention bidirectional-long short-term memory (MHA-BiLSTM).
Journal ArticleDOI
Gated recurrent unit least-squares generative adversarial network for battery cycle life prediction
TL;DR: In this paper , a least-squares generative adversarial network with the gated recurrent unit as the generator and multi-layer perceptron as the discriminator is used to predict the remaining useful life of lithium-ion batteries.
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.
Proceedings ArticleDOI
Learning Phrase Representations using RNN Encoder--Decoder for Statistical Machine Translation
Kyunghyun Cho,Bart van Merriënboer,Caglar Gulcehre,Dzmitry Bahdanau,Fethi Bougares,Holger Schwenk,Yoshua Bengio,Yoshua Bengio,Yoshua Bengio +8 more
TL;DR: In this paper, the encoder and decoder of the RNN Encoder-Decoder model are jointly trained to maximize the conditional probability of a target sequence given a source sequence.
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A Survey on Transfer Learning
Sinno Jialin Pan,Qiang Yang +1 more
TL;DR: The relationship between transfer learning and other related machine learning techniques such as domain adaptation, multitask learning and sample selection bias, as well as covariate shift are discussed.
Journal ArticleDOI
A review on machinery diagnostics and prognostics implementing condition-based maintenance
TL;DR: This paper attempts to summarise and review the recent research and developments in diagnostics and prognostics of mechanical systems implementing CBM with emphasis on models, algorithms and technologies for data processing and maintenance decision-making.
Journal ArticleDOI
Remaining useful life estimation - A review on the statistical data driven approaches
TL;DR: This paper systematically reviews the recent modeling developments for estimating the RUL and focuses on statistical data driven approaches which rely only on available past observed data and statistical models.