Results 11 to 20 of about 20,775 (227)

A Novel RUL-Centric Data Augmentation Method for Predicting the Remaining Useful Life of Bearings

Machines
Maintaining the reliability of rotating machinery in industrial environments entails significant challenges. The objective of this paper is to develop a methodology that can accurately predict the condition of rotating machinery in order to facilitate ...
Miao He, Zhonghua Li, Fangchao Hu
doaj   +2 more sources

Turbofan Engine Remaining Useful Life (RUL) Prediction Based on Bi-Directional Long Short-Term Memory (BLSTM)

arXiv.org
The aviation industry is rapidly evolving, driven by advancements in technology. Turbofan engines used in commercial aerospace are very complex systems. The majority of turbofan engine components are susceptible to degradation over the life of their operation. Turbofan engine degradation has an impact to engine performance, operability, and reliability.
Abedin Sherifi
openaire   +3 more sources

FDBRP: A Data–Model Co-Optimization Framework Towards Higher-Accuracy Bearing RUL Prediction [PDF]

Sensors
This paper proposes Feature fusion and Dilated causal convolution model for Bearing Remaining useful life Prediction (FDBRP), an integrated framework for accurate Remaining Useful Life (RUL) prediction of rolling bearings that combines three key ...
Muyu Lin, Qing Ye, Shiyue Na, Dongmei Qin, Xiaoyu Gao, Qiang Liu   +5 more
doaj   +2 more sources

Fault Diagnosis and Prediction of Remaining Useful Life (RUL) of Rolling Element Bearing : A review state of art [PDF]

Tribologia - Finnish Journal of Tribology
Fault diagnosis of rolling element bearings is a critical aspect of machine maintenance and reliability. Bearings are extensively used in various industrial applications, and their failure can lead to costly downtime and equipment damage.
Mogal, Shyam, P. B. Kushare, R. V. Bhandare, V. M. Phalle   +3 more
core   +4 more sources

A Multi-Class Classification Based Approach for Remaining Useful Life (RUL) Prediction of Li-Ion Battery

Bioscience Biotechnology Research Communications, 2020
Yuvraj Gupta, Partho Ghosh, Dahigaonkar D.J., Dwaramwar P.A.   +3 more
semanticscholar   +3 more sources

XGBoost-Based Remaining Useful Life Estimation Model with Extended Kalman Particle Filter for Lithium-Ion Batteries

Sensors, 2022
The instability and variable lifetime are the benefits of high efficiency and low-cost issues in lithium-ion batteries.An accurate equipment’s remaining useful life prediction is essential for successful requirement-based maintenance to improve ...
Sadiqa Jafari, Yung-Cheol Byun
doaj   +1 more source

Three-Stage Wiener-Process-Based Model for Remaining Useful Life Prediction of a Cutting Tool in High-Speed Milling

Sensors, 2022
Tool condition monitoring can be employed to ensure safe and full utilization of the cutting tool. Hence, remaining useful life (RUL) prediction of a cutting tool is an important issue for an effective high-speed milling process-monitoring system ...
Weichao Liu, Wen-An Yang, Youpeng You
doaj   +1 more source

Shapelet selection based on a genetic algorithm for remaining useful life prediction with supervised learning

Heliyon, 2022
RUL (remaining useful life) shapelets were recently developed to overcome the shortcomings of similarity-based RUL prediction methods, such as high sensitivity to parameters.
Gilseung Ahn, Min-Ki Jin, Seok-Beom Hwang, Sun Hur   +3 more
doaj   +1 more source

Long Short-Term Memory Neural Network with Transfer Learning and Ensemble Learning for Remaining Useful Life Prediction

Sensors, 2022
Prediction of remaining useful life (RUL) is greatly significant for improving the safety and reliability of manufacturing equipment. However, in real industry, it is difficult for RUL prediction models trained on a small sample of faults to obtain ...
Lixiong Wang, Hanjie Liu, Zhen Pan, Dian Fan, Ciming Zhou, Zhigang Wang   +5 more
doaj   +1 more source

Tool wear mechanism, monitoring and remaining useful life (RUL) technology based on big data: a review

SN Applied Sciences, 2022
Tool wear is a key factor affecting many aspects of metal cutting machining, including surface quality, machining efficiency and tool life. As machining continues to evolve towards intelligence, hot spots and trends in tool wear-related research are also
Yang Zhou, Changfu Liu, Xinli Yu, Bo Liu, Yu Quan   +4 more
semanticscholar   +1 more source