Results 101 to 110 of about 9,526,985 (202)

Attentive Bidirectional Long Short-Term Memory Model to Predict the Remaining Useful Life of Aircraft Engines

Accurate Remaining Useful Life (RUL) prediction for turbofan engines remains a challenging issue for the aviation industry due to operational condition variability, sensor noise, and the relatively complex degradation patterns of turbofans.
Deo, Ravinesh C., Seixas Leal, Andreia, Ali, Mumtaz, Prasad, Salvin Sanjesh, Berton, Lilian, De Castro Santos, Luis Carlos   +5 more
core   +1 more source

Prognostic Prediction Of Remaining Useful Life In High Dimensional Gas Turbine Using Enhanced Deep Convolutional Neural Network

, 2022
Accurately predicting the remaining useful life (RUL) of the rotary machines is of great significance for improving the reliability and safety of the industrial mechanical ...
Abdulraheem, Amgad Muneer Othman
core  

Development of 2D CNN-based Model for Remaining Useful Life of Turbofan Engine

, 2023
In this paper, we propose a method for predicting Remaining Useful Life (RUL) using multivariate time series data. To enhance the relationships between the input and output data, we propose a pre-processing method and construct a model using 2D ...
김해영, 구교권, 백민석, 반재필   +3 more
core  

DFL-RUL: Decentralised Federated Learning for Battery Remaining Useful Life Estimation on Heterogeneous Edge-to-cloud

Accurate remaining useful life (RUL) prediction of lithium-ion batteries is essential for reliable and cost-effective electric vehicle operation, yet existing approaches largely rely on centralised training or overlook deployment constraints and data ...
Niri, Mona Faraji, El-Dalahmeh, Mo’ath, Marco, James, Pournazari, Jaber, Park, Dong-Hwan, Dinh, Truong Quang, Ahn, Jung-Hoon   +6 more
core   +1 more source

An Improved Method for Predicting the Remaining Useful Life Using a Spatial–Temporal Feature Extraction Network With Attention Mechanism

IEEE Access
The prediction of the remaining useful life (RUL) of mechanical equipment is of vital importance to its operation and maintenance. Deep learning methods can effectively extract degradation information closely related to equipment RUL from extensive ...
Xiaojia Yan, Weige Liang, Shiyan Sun
doaj   +1 more source

FOG COMPUTING BASED BEARING REMAINING USEFUL LIFE PROGNOSIS USING TIME SERIES NORMALIZED SIMILARITY AND RECURRENT NEURAL NETWORKS [PDF]

, 2019
Techniques are described for determining a remaining useful life (RUL) prognosis of bearings using a feature extraction module for extracting time series normalized similarity (TSNS) features for vibration data normalization and a prediction module ...
Jiao, Cheng, Liu, Tonny
core  

Assessment of overall remaining useful life of lubricants by integrating oil quality and performance

Heliyon
Effective lubricant health monitoring programs are essential for extending the lifespan of both the lubricant and machinery. An accurate and reliable remaining useful life (RUL) prediction is necessary for maintenance decision support. The degradation of
Wasan Chokelarb, Pongsert Sriprom, Lasuardi Permana, Pornsawan Assawasaengrat   +3 more
doaj   +1 more source

Cyclic monitoring of the Remaining Useful Life RUL for the Bearing fault prognosis

, 2023
Over the last decades, Prognostics has played a dominant role in preventive maintenance inmanufacturing. It usually involves estimating the Remaining Useful Life (RUL) or the Time to Failure (TTF)of mechanical systems. In Prognosis, the analysis could often be purely data-driven (Trend Analysis). Itrequires a vast data set and offers the double benefit
openaire   +1 more source

A Real-Time Diagnostic System Using a Long Short-Term Memory Model with Signal Reshaping Technology for Ship Propellers

Sensors
This study develops a ship propeller diagnostic system to address the issue of insufficient ship maintenance capacity and enhance operational efficiency.
Sheng-Chih Shen, Chih-Chieh Chao, Hsin-Jung Huang, Yi-Ting Wang, Kun-Tse Hsieh   +4 more
doaj   +1 more source

Prediction of remaining useful life (RUL) of Komatsu excavator under reliability analysis in the Weibull-frailty model. [PDF]

PLoS One, 2020
Ghomghaleh A, Khaloukakaie R, Ataei M, Barabadi A, Nouri Qarahasanlou A, Rahmani O, Beiranvand Pour A.   +6 more
europepmc   +1 more source