Pyramid LSTM Network for Tool Condition Monitoring
Author:
Affiliation:
1. Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Changzhou, China
2. School of Machinery and Automation, Wuhan University of Science and Technology, Wuhan, China
Funder
Chinese National Key Research and Development Project
Chinese Ministry of Science and Technology
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Instrumentation
Link
http://xplorestaging.ieee.org/ielx7/19/9717300/09770792.pdf?arnumber=9770792
Reference36 articles.
1. Fuzzy neural network modelling for tool wear estimation in dry milling operation;li;Proc Annu Conf PHM Soc,2009
2. Autoencoder Regularized Network For Driving Style Representation Learning
3. A Review of Recurrent Neural Networks: LSTM Cells and Network Architectures
4. LSTM-based encoder-decoder for multi-sensor anomaly detection;malhotra;arXiv 1607 00148,2016
5. Effects of calcium-treatment of a plastic injection mold steel on the tool wear and power consumption in slot milling
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