Results 31 to 40 of about 530,506 (256)

Integrated Bayesian Framework for Remaining Useful Life Prediction.

, 2014
International audienceIn this paper, a data-driven method for remaining useful life (RUL) prediction is presented. The method learns the relation between acquired sensor data and end of life time (EOL) to predict the RUL.
Medjaher, Kamal, Mosallam, Ahmed, Zerhouni, Noureddine   +2 more
core   +2 more sources

Predicting the Remaining Useful Life of Supercapacitors under Different Operating Conditions

Energies
With the rapid development of the new energy industry, supercapacitors have become key devices in the field of energy storage. To forecast the remaining useful life (RUL) of supercapacitors, we introduce a new technology that integrates variational mode ...
Guangheng Qi, Ning Ma, Kai Wang
doaj   +1 more source

Digital Twin and Data-Driven Remaining Useful Life Prediction of Gearbox

IEEE Access
Traditional approaches for predicting the remaining useful life (RUL) of gearboxes often face challenges in integrating physical and virtual data, leading to reduced prediction accuracy and an increased risk of system failure.
Quanbo Lu, Mei Li, Xiaojuan Huang
doaj   +1 more source

Aeroengines Remaining Useful Life Prediction Based on Improved C-Loss ELM

IEEE Access, 2020
A correntropy induced loss (C-loss) function is a loss function developed based on entropy theory. Due to the non-convexity of the C-loss function, ELM based on C-loss has been proven to have better regression effects and robustness.
Bowen Zhang, Yanjun Li, Yu Bai, Yuyuan Cao   +3 more
doaj   +1 more source

Online Bearing Remaining Useful Life Prediction Based on a Novel Degradation Indicator and Convolutional Neural Networks

, 2018
In industrial applications, nearly half the failures of motors are caused by the degradation of rolling element bearings (REBs). Therefore, accurately estimating the remaining useful life (RUL) for REBs are of crucial importance to ensure the reliability
Cheng, Cheng, Ding, Han, Ma, Guijun, Sun, Mingyang, Teng, Fei, Yuan, Ye, Zhang, Yong   +6 more
core   +1 more source

A Similarity-Based Prognostics Approach for Remaining Useful Life Prediction [PDF]

, 2014
Physics-based and data-driven models are the two major prognostic approaches in the literature with their own advantages and disadvantages. This paper presents a similarity-based data-driven prognostic methodology and efficiency analysis study on ...
Camci, Faith, Eker, Ömer Faruk, Jennions, Ian K.   +2 more
core   +1 more source

Stochastic RUL calculation enhanced with TDNN-based IGBT failure modeling [PDF]

, 2016
Power electronics are widely used in the transport and energy sectors. Hence, the reliability of these power electronic components is critical to reducing the maintenance cost of these assets.
Alghassi, Alireza, Perinpanayagam, Suresh, Samie, Mohammad   +2 more
core   +1 more source

Financial Burden Associated With Hospitalisation Among Families of Childhood Brain Tumours in Australia

Pediatric Blood &Cancer, EarlyView.
ABSTRACT Background Families of children with cancer experience significant financial strain, even with universal healthcare. Indirect costs, such as productivity losses and non‐medical expenses, are rarely included in economic evaluations, and little is known about how effectively financial aid programmes alleviate this burden. Childhood brain tumours
Megumi Lim, Natalie Bradford, David Brain, Sameera Senanayake, Sanjeewa Kularatna, Kate Young, Christine Cashion, Tim Hassall, Susanna Cramb   +8 more
wiley   +1 more source

Attention-Gaussian-LSTM-Wiener based remaining useful life prediction method

Autonomous Intelligent Systems
Most machine learning-based remaining useful life (RUL) prediction methods only yield point predictions, and their “black-box” nature results in low interpretability.
Shuiyuan Cao, Liguo Qin, Hanwen Zhang, Aiming Wang, Jun Shang   +4 more
doaj   +1 more source

LSTM-Based Battery Remaining Useful Life Prediction With Multi-Channel Charging Profiles

IEEE Access, 2020
Remaining useful life (RUL) prediction of lithium-ion batteries can reduce the risk of battery failure by predicting the end of life. In this paper, we propose novel RUL prediction techniques based on long short-term memory (LSTM).
Kyungnam Park, Yohwan Choi, Won Jae Choi, Hee-Yeon Ryu, Hongseok Kim   +4 more
doaj   +1 more source