Results 71 to 80 of about 9,187,359 (195)

Remaining Useful Life Prediction Method for Electric Submersible Pump System Based on DA-RNN

Shiyou jixie
Electric submersible pump （ESP） is the main artificial lift equipment in offshore oilfields. Its operation and maintenance costs are extremely high. Once it gets out of order， it will cause losses to oilfield operations.
Yu Jifei, Ji Yuchen, Lu Xin, Sui Xianfu, Peng Jianlin, Han Guoqing, Yang Yang   +6 more
doaj   +3 more sources

Artificial intelligence–driven decoupling structure–activity relationship for lithium‐ion batteries

InfoScience, Volume 3, Issue 3, September 2026.
Artificial intelligence can efferently accelerate the high‐throughput screening of battery materials, the analysis of multiphase mechanisms, and the precise prediction of capacity and cycle life. This review systematically summarizes the applications of machine learning (ML) in decoupling the complex structure‐activity relationships of lithium‐ion ...
Tao Wang, Yuhang Meng, Tianhao Chen, Xiangfen Jiang, Yoshio Bando, Jinjue Zeng, Xuebin Wang   +6 more
wiley   +1 more source

Shapelet-based remaining useful life estimation.

, 2014
International audienceIn the Prognostics and Health Management domain, estimating the remaining useful life (RUL) of critical machinery is a challenging task.
Morello, Brigitte, Zerhouni, Noureddine, Simon Malinowski, Brigitte Chebel-Morello, Noureddine Zerhouni, Malinowski, Simon   +5 more
core   +1 more source

Wind Turbine Blade Crack Detection and Assessment in Images Using Machine Learning

Wind Energy, Volume 29, Issue 9, September 2026.
ABSTRACT As the wind energy industry matures, inspection of wind turbine blades (WTBs) is shifting from a manual process involving rope access and grading of damage, towards unmanned aerial vehicle (UAV) photography and artificial intelligence‐aided processing of data.
Callum Rothon, Nina Dethlefs, Robert C. Houseago   +2 more
wiley   +1 more source

Remaining Useful Life estimation based on discriminating shapelet extraction.

, 2015
International audienceIn the Prognostics and Health Management (PHM) domain, estimating the remaining useful life (RUL) of critical machinery is a challenging task.
Zerhouni, Noureddine, Chebel-Morello, Brigitte, Malinowski, Simon   +2 more
core   +1 more source

Time Series Domain Adaptation: A Review

WIREs Data Mining and Knowledge Discovery, Volume 16, Issue 3, September 2026.
This survey provides a comprehensive and systematic review of TSDA methods from the perspectives of access‐privacy constraints, category‐space semantics, and source‐target topology, three orthogonal axes that unify existing approaches within a common taxonomy.
M. T. Furqon, Mahardhika Pratama, Wisnu Jatmiko   +2 more
wiley   +1 more source

Anomaly-informed remaining useful life estimation (AIRULE) of bearing machinery using deep learning framework

MethodsX
A rolling bearing is a crucial element within rotating machinery, and its smooth operation profoundly influences the overall well-being of the equipment.
Pooja Kamat, Satish Kumar, Shruti Patil, Ketan Kotecha   +3 more
doaj   +1 more source

Prognostic modelling options for remaining useful life estimation by industry

, 2011
Over recent years a significant amount of research has been undertaken to develop prognostic models that can be used to predict the remaining useful life of engineering assets.
M. Hodkiewicz, J.Z. Sikorska, Hodkiewicz, Melinda, L. Ma, Ma, Lin, Sikorska, Joanna   +5 more
core   +1 more source

Remaining useful life estimation : Review

, 2014
This paper reviews the recent modelling developments in estimating the remaining useful life (RUL) of industrial systems. The RUL estimation models are categorized into experimental, data driven, physics based and hybrid approaches.
Ahmadzadeh, Farzaneh,, Lundberg, Jan,
core   +1 more source

Correction: Prediction of remaining useful life (RUL) of Komatsu excavator under reliability analysis in the Weibull-frailty model. [PDF]

PLoS One, 2021
Ghomghaleh A, Khaloukakaie R, Ataei M, Barabadi A, Qarahasanlou AN, Rahmani O, Pour AB.   +6 more
europepmc   +1 more source