Journal ArticleDOI
A novel approach for predicting tool remaining useful life using limited data
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TLDR
A novel method in which a deep bidirectional long short-term memory neural network in which sequential data are predicted and smoothed by forwards and backwards directions, respectively, is developed to encode temporal information and identify long-term dependencies is proposed.About:
This article is published in Mechanical Systems and Signal Processing.The article was published on 2020-09-01. It has received 38 citations till now. The article focuses on the topics: Machine tool & Predictive maintenance.read more
Citations
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Journal ArticleDOI
Artificial intelligence systems for tool condition monitoring in machining: analysis and critical review
Danil Yu. Pimenov,Andres Bustillo,Szymon Wojciechowski,Vishal S. Sharma,Manish Gupta,Mustafa Kuntoğlu +5 more
Journal ArticleDOI
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.
Journal ArticleDOI
An online transfer learning-based remaining useful life prediction method of ball bearings
TL;DR: An online transfer learning method for remaining useful life (RUL) prediction of bearings is proposed and RUL of specified bearings can be estimated precisely by the established model.
Journal ArticleDOI
A data-driven approach for tool wear recognition and quantitative prediction based on radar map feature fusion
Xuebing Li,Xianli Liu,Cai Xu Yue,Shaoyang Liu,Bowen Zhang,Rongyi Li,Steven Y. Liang,Lihui Wang +7 more
TL;DR: Experimental results demonstrated that the proposed approach could recognize the current wear state quickly and accurately whilst predicting wear values based on limited historical data available, contributing to making a more flexible tool replacement decision in intelligent manufacturing processes.
Journal ArticleDOI
Application of Measurement Systems in Tool Condition Monitoring of Milling: A Review of measurement science approach
Danil Yu. Pimenov,Munish Kumar Gupta,Leonardo Rosa Ribeiro da Silva,M. Raj Kiran,Navneet Khanna,Grzegorz Królczyk +5 more
TL;DR: In this paper , the advantages, disadvantages, and prospects of using such sensors for milling operations are discussed in this review article, as well as the use of conventional and artificial intelligence (AI) of sensory systems.
References
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Least angle regression
Bradley Efron,Trevor Hastie,Iain M. Johnstone,Robert Tibshirani,Hemant Ishwaran,Keith Knight,Jean-Michel Loubes,Jean-Michel Loubes,Pascal Massart,Pascal Massart,David Madigan,David Madigan,Greg Ridgeway,Greg Ridgeway,Saharon Rosset,Saharon Rosset,Ji Zhu,Robert A. Stine,Berwin A. Turlach,Sanford Weisberg +19 more
TL;DR: A publicly available algorithm that requires only the same order of magnitude of computational effort as ordinary least squares applied to the full set of covariates is described.
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LSTM: A Search Space Odyssey
TL;DR: This paper presents the first large-scale analysis of eight LSTM variants on three representative tasks: speech recognition, handwriting recognition, and polyphonic music modeling, and observes that the studied hyperparameters are virtually independent and derive guidelines for their efficient adjustment.
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
Learning to Forget: Continual Prediction with LSTM
TL;DR: This work identifies a weakness of LSTM networks processing continual input streams that are not a priori segmented into subsequences with explicitly marked ends at which the network's internal state could be reset, and proposes a novel, adaptive forget gate that enables an LSTm cell to learn to reset itself at appropriate times, thus releasing internal resources.
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.
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