Results 101 to 110 of about 1,643,808 (218)

Contrastive, Autoencoding, and Variational Representations for Telemetry-Driven RUL Prediction

Predictive Maintenance in safety-critical industries relies on accurate Remaining Useful Life (RUL) estimation from multivariate telemetry. Still real-world datasets are often dominated by censored observations and frequently lack explicit failure ...
Mahmoud Rahat
core   +1 more source

Noise-conditioned denoising autoencoder with temporal attention for bearing RUL prediction [PDF]

Bearings are important elements of mechanical systems and the correct forecasting of their remaining useful life (RUL) is key to successful predictive maintenance.
Syntetos, Aris, Chen, Chong, Liu, Ying, Jin, Zhongtian, Chong Chen, Aris Syntetos, Ying Liu, Zhongtian Jin   +7 more
core   +1 more source

Parallel simulation based adaptive prediction for equipment remaining useful life [PDF]

, 2018
The latest demands for remaining useful life (RUL) prediction are online prediction, real-time prediction and adaptive prediction. This paper addresses the demands of RUL prediction and proposes a novel framework of parallel simulation based adaptive ...
Chenglong Ge, Zhihua Dong, Yuanchang Zhu, Yanqiang Di, Ge, Chenglong, Zhu, Yuanchang, Di, Yanqiang, Dong, Zhihua   +7 more
core   +1 more source

Remaining Useful Life Prediction for Rotating Machinery Based on Optimal Degradation Indicator

Shock and Vibration, 2017
Remaining useful life (RUL) prediction can provide early warnings of failure and has become a key component in the prognostics and health management of systems.
Aisong Qin, Qinghua Zhang, Qin Hu, Guoxi Sun, Jun He, Shuiquan Lin   +5 more
doaj   +1 more source

Deep Learning-based Health Indicator for Better Bearing RUL Prediction

, 2021
© INTER-NOISE 2021 .All right reserved.The prognostic performance of data-driven approaches closely depends on the features extracted from the measurement.
김태완, LEE, SEUNGCHUL
core  

Prediction of the Remaining Useful Life of Bearings Through CNN-Bi-LSTM-Based Domain Adaptation Model

Sensors
Predicting the remaining useful life (RUL) of mechanical bearings is crucial in the industry. Estimating the RUL enables the assessment of health bearing, maintenance planning, and significant cost reduction, thereby fostering industrial development ...
Feifan Li, Zhuoheng Dai, Lei Jiang, Chanfei Song, Caiming Zhong, Yingna Chen   +5 more
doaj   +1 more source

RUL prediction based on a new similarity-instance based approach.

, 2014
International audiencePrognostics is a major activity of Condition-Based Maintenance (CBM) in many industrial domains where safety,reliability and cost reduction are of high importance. The main objective of prognostics is to provide an estimation of the
Morello, Brigitte, Zerhouni, Noureddine, Khelif, Racha, Racha Khelif, Simon Malinowski, Brigitte Chebel-Morello, Noureddine Zerhouni, Malinowski, Simon   +7 more
core   +1 more source

Online Fault Tolerant RUL Prediction Strategy for Lithium-Ion Batteries Using Machine Learning

The deterioration of lithium-ion batteries can lead to electrical system failures and potentially catastrophic consequences. Consequently, predicting the remaining useful life (RUL) of batteries is essential to prevent such failures and related issues ...
Chafik Okar, Mohamed Mansouri, Hicham Chaoui, Brahim Zraibi   +3 more
core   +1 more source

Research on Bearing Remaining Useful Life Prediction Method Based on Double Bidirectional Long Short-Term Memory

Applied Sciences
The predictive capability of traditional bearing remaining useful life (RUL) prediction models is insufficient, and the prediction networks lack universality, leading to unsatisfactory results in predicting the RUL of bearings, which leads to untimely ...
Yi Zou, Wenlei Sun, Hongwei Wang, Tiantian Xu, Bingkai Wang   +4 more
doaj   +1 more source

A joint LSTM-XGBoost-based method for predicting the state of health and remaining useful life of lithium batteries

电力工程技术
The rapid development of the new energy vehicle field poses greater demands on the state of health (SOH) monitoring and remaining useful life (RUL) estimation of lithium batteries for energy storage.
Xiaoxin DENG, Sihang GAO, Jiajia CHEN, Yuqing LEI, Weisheng HE   +4 more
doaj   +1 more source