Getting a Better Sense of Data Drift in Dynamic Systems: Sequence-Based Deep Learning for Monitoring Slowly Evolving Degradation Processes-Reference-Cited by-同舟云学术

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Getting a Better Sense of Data Drift in Dynamic Systems: Sequence-Based Deep Learning for Monitoring Slowly Evolving Degradation Processes

Published:2023-11-15 Issue: Volume:9 Page:70
ISSN:
Container-title:ECSA 2023
language:
Short-container-title:

Author:

Berghout Tarek¹^ORCID,Benbouzid Mohamed²³^ORCID

Affiliation:

1. Laboratory of Automation and Manufacturing Engineering, University of Batna 2, Batna 05000, Algeria

2. UMR CNRS 6027 IRDL, University of Brest, 29238 Brest, France

3. Logistics Engineering College, Shanghai Maritime University, Shanghai 201306, China

Publisher

MDPI

Link

https://www.mdpi.com/2673-4591/58/1/70/pdf

Reference9 articles.

1. Berghout, T., and Benbouzid, M. (2022). A Systematic Guide for Predicting Remaining Useful Life with Machine Learning. Electronics, 11.

2. Discussion and Review on Evolving Data Streams and Concept Drift Adapting;Khamassi;Evol. Syst.,2018

3. Machinery Health Prognostics: A Systematic Review from Data Acquisition to RUL Prediction;Lei;Mech. Syst. Signal Process.,2018

4. Concept Drift Adaptation for Time Series Anomaly Detection via Transformer;Ding;Neural Process. Lett.,2023

5. Sequence to Sequence Learning with Neural Networks;Sutskever;Adv. Neural Inf. Process. Syst.,2014

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