Results 111 to 120 of about 18,585 (221)

Major challenges in prognostics: study on benchmarking prognostic datasets [PDF]

, 2012
Even though prognostics has been defined to be one of the most difficult tasks in Condition Based Maintenance (CBM), many studies have reported promising results in recent years.
Camci, Faith, Eker, Ömer Faruk, Jennions, Ian K.   +2 more
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

Multi-Head Self-Attention-Based Fully Convolutional Network for RUL Prediction of Turbofan Engines

Algorithms
Remaining useful life (RUL) prediction is widely applied in prognostic and health management (PHM) of turbofan engines. Although some of the existing deep learning-based models for RUL prediction of turbofan engines have achieved satisfactory results ...
Zhaofeng Liu, Xiaoqing Zheng, Anke Xue, Ming Ge, Aipeng Jiang   +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

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 Prediction Based on Deep Learning: A Survey

Sensors
Remaining useful life (RUL) is a metric of health state for essential equipment. It plays a significant role in health management. However, RUL is often random and unknown.
Fuhui Wu, Qingbo Wu, Yusong Tan, Xinghua Xu   +3 more
doaj   +1 more source

A Comprehensive Review of Artificial Intelligence-Based Algorithms for Predicting the Remaining Useful Life of Equipment

Sensors
In the contemporary big data era, data-driven prognostic and health management (PHM) methodologies have emerged as indispensable tools for ensuring the secure and reliable operation of complex equipment systems.
Weihao Li, Jianhua Chen, Sijuan Chen, Peilin Li, Bing Zhang, Ming Wang, Ming Yang, Jipu Wang, Dejian Zhou, Junsen Yun   +9 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  

Prognostic and health management design for subsea applications [PDF]

, 2018
Alkali, Babakalli, Gao, Xiaojing, McGlinchey, Don, Niculita, Octavian   +3 more
core   +1 more source

RUL prediction method based on sequential health index evaluation with multidimensional coupled degradation data. [PDF]

PLoS One
Han F, Mo B.
europepmc   +1 more source