Results 111 to 120 of about 1,541,566 (221)

Deep learning-stochastic ensemble for RUL prediction and predictive maintenance with dynamic mission abort policies

Accurate prediction of Remaining Useful Life (RUL) is crucial for optimizing maintenance strategies in industrial systems. However, existing models often falter under nonlinear and nonstationary degradation conditions with stochastic and abrupt failures,
Rizwan, U., A., Faizanbasha
core   +3 more sources

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

Remaining Useful Life (RUL) Prediction of Equipment in Production Lines Using Artificial Neural Networks. [PDF]

Sensors (Basel), 2021
Kang Z, Catal C, Tekinerdogan B.
europepmc   +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

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

Indirect Prediction of Lithium-Ion Battery RUL Based on CEEMDAN and CNN-BiGRU

Predicting the remaining useful life (RUL) of lithium-ion batteries is crucial for enhancing their reliability and safety. Addressing the issue of inaccurate RUL predictions caused by the nonlinear decay resulting from capacity regeneration, this paper ...
Kai Lv, Guangchen Liu, Zhiqiang Ma, Caijilahu Bao   +3 more
core   +1 more source

A Novel Remaining Useful Life Prediction Approach Combined eXtreme Gradient Boosting and Multi-Quantile Recurrent Neural Network

IEEE Access
Remaining useful life (RUL) prediction is a key technology to ensure the reliability and safety of high-end equipment. Deep learning is widely used for RUL prediction due to the excellent feature extraction ability and nonlinear fitting ability ...
Yantao Yin, Jianyin Zhao, Xiao Zhang, Ying Liu   +3 more
doaj   +1 more source

Remaining Useful Life Prediction of Rolling Bearings Based on Policy Gradient Informer Model

工程科学与技术
As a typical encoder–decoder, the transformer architecture has inherent limitations such as secondary time complexity, high memory usage, and a complex model structure; these issues can lead to lower prediction accuracy and decreased computational ...
Jiahao XIONG, Feng LI, Baoping TANG, Yongchao WANG, Ling LUO   +4 more
doaj  

Hybrid framework for Remaining Useful Life (RUL) prediction of rolling bearing faults

Remaining Useful Life (RUL) prediction is critical for preventing catastrophic failures in industrial systems, enabling efficient maintenance scheduling and resource optimization.
Arslan Shaukat, Ali Saeed, Salman Jan, Adeel M. Syed, M. Usman Akram, Muazzam A. Khan, Toqeer Ali Syed   +6 more
core   +1 more source

A hybrid CNN-DNN model for battery remaining useful life RUL prediction. [PDF]

Sci Rep
Khoufi H, Bouazizi E, Khedr AE, Elshewey AM.   +3 more
europepmc   +1 more source