Zobrazeno 1 - 3
of 3
pro vyhledávání: '"Djillali Guebli"'
Publikováno v:
ISA Transactions. 113:28-38
Efficiency and robustness in remaining useful life (RUL) prediction are crucial in system health monitoring. Thus, the internal logic computation of a Deep LSTM model for RUL prediction is mainly shaped and evaluated over a training data-set and its
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ea7a9e3be4d589d694385c47edd95275
https://doi.org/10.1016/j.isatra.2020.07.003
https://doi.org/10.1016/j.isatra.2020.07.003
Publikováno v:
Automatic Control and Computer Sciences. 55:15-25
This paper introduces a new deep learning model for Remaining Useful Life (RUL) prediction of complex industrial system components using Gaussian Mixture Models (GMMs). The used model is an enhanced deep LSTM approach, for which Gaussian mixture clus
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::618705d208b34c5f456360dd569143ec
https://doi.org/10.3103/s0146411621010089
https://doi.org/10.3103/s0146411621010089
Publikováno v:
2020 Prognostics and Health Management Conference (PHM-Besançon).
This paper proposes a clustering based deep learning network to predict Remaining Useful Life or RUL for a system component. This RUL means length from current time to end of component useful life time. The objective of our approach is to perform in
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d624d014e22d9e621bb3d3a237bca0db
https://doi.org/10.1109/phm-besancon49106.2020.00049
https://doi.org/10.1109/phm-besancon49106.2020.00049