Results 191 to 200 of about 20,775 (227)

A Remaining Useful Life Prediction Method of Rolling Bearings Based on Deep Reinforcement Learning

IEEE Internet of Things Journal
Remaining useful life (RUL) prediction technology is a crucial task in prognostics and health management (PHM) systems, as it contributes to the enhancement of the reliability of equipment operation. With the development of Industrial Internet of Things (
Guokang Zheng, Yasong Li, Zheng Zhou, Ruqiang Yan   +3 more
semanticscholar   +1 more source

A comprehensive survey of machine remaining useful life prediction approaches based on pattern recognition: taxonomy and challenges

Measurement science and technology
Predictive maintenance (PdM) is currently the most cost-effective maintenance method for industrial equipment, offering improved safety and availability of mechanical assets.
Jianghong Zhou, Jiahong Yang, Quan Qian, Yi Qin   +3 more
semanticscholar   +1 more source

Uncertainty-Weighted Domain Generalization for Remaining Useful Life Prediction of Rolling Bearings Under Unseen Conditions

IEEE Sensors Journal
Domain adaptation (DA) methods are widely used in rolling bearing remaining useful life (RUL) prediction to align source and target domains. However, DA relies on previously collected target datasets, which are not always available in practical ...
Shiyan Tong, Yan Han, Xiaolong Zhang, Hao Tian, Xin Li, Qingqing Huang   +5 more
semanticscholar   +1 more source

Adaptive Two-Stage Model for Bearing Remaining Useful Life Prediction Using Gaussian Process Regression With Matched Kernels

IEEE Transactions on Reliability
Stemming from complex mechanisms and working conditions of bearings, single degradation models often fail to adequately describe complex degradation process and provide reliable prediction of remaining useful life (RUL).
Xinyu Zheng, Wei Fan, Chao Chen, Zhike Peng   +3 more
semanticscholar   +1 more source

Machine Learning-Based Approach for Predicting Battery Remaining Useful Life (RUL): A Data-Driven Methodology

2023 International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS), 2023
B. Devi, V. Suresh Kumar
openaire   +1 more source

Optimizing the Maintenance Strategy Through Predicting Remaining Useful Life (RUL) for Critical Medical Equipment Based on Degradation Analysis

2020
Modern medical equipment and devices have become very sophisticated and complex. These assets are expected to operate under rigorous environments. Hospitals need to confirm that their critical assets particularly medical equipment are reliable, safe, accurate and operating with high level of performance.
openaire   +1 more source

Multidimensional Attention Domain Adaptive Method Incorporating Degradation Prior for Machine Remaining Useful Life Prediction

IEEE Transactions on Industrial Informatics
Machinery remaining useful life (RUL) prediction has important guiding significance for prognostics and health management. In order to improve the prediction accuracy of the RUL prediction model under different working conditions, the transfer method on ...
Shushuai Xie, Wei Cheng, Zelin Nie, Ji Xing, Xuefeng Chen, Lin Gao, Zhao Xu, Rongyong Zhang   +7 more
semanticscholar   +1 more source

Multiscale BLS-Based Lightweight Prediction Model for Remaining Useful Life of Aero-Engine

IEEE Transactions on Reliability
Remaining useful life (RUL) prediction of aero-engines is one of the important issues in research related to engine health management. Although deep learning has made great progress in fault diagnosis research, successful training of deep learning models
Tiantian Xu, Guangjie Han, Hongbo Zhu, Chuan Lin, Jinlin Peng   +4 more
semanticscholar   +1 more source

Machine Learning Techniques for Predicting Remaining Useful Life (RUL) of Machinery for Sustainable Manufacturing Lines

2023
Lim Khai Sian, J. Joshua Thomas
openaire   +1 more source

Development of Remaining Useful Life (RUL) Prediction of Lithium-ion Battery Using Genetic Algorithm-Deep Learning Neural Network (GADNN) Hybrid Model

2022 7th International Conference on Electric Vehicular Technology (ICEVT), 2022
Muchamad Iman Karmawijaya, Irsyad Nashirul Haq, Edi Leksono, Augie Widyotriatmo   +3 more
openaire   +1 more source