Remaining Useful Life (RUL) Prediction of Equipment in Production Lines Using Artificial Neural Networks [PDF]
Sensors, 2021 Predictive maintenance of production lines is important to early detect possible defects and thus identify and apply the required maintenance activities to avoid possible breakdowns.
Ziqiu Kang +2 more
doaj +8 more sources
RUL-RVE: interpretable assessment of Remaining Useful Life [PDF]
Software Impacts, 2022 This work has been partially supported by the Ministry of Economy, Industry and Competitiveness (“Ministerio de Economía, Industria Competitividad”) from Spain /FEDER under grant PID2020-112726-RB-I00 and by Principado de Asturias, grant SV-PA-21-AYUD ...
Costa Cortéz, Nahuel Alejandro +1 more
core +5 more sources
Remaining Useful Life (RUL) Prediction Based on the Bivariant Two-Phase Nonlinear Wiener Degradation Process [PDF]
EntropyRecent advancements in science and technology have resulted in products with enhanced reliability and extended lifespans across the aerospace and related sectors.
Lijun Sun, Yuying Liang, Zaizai Yan
doaj +4 more sources
A Novel RUL-Centric Data Augmentation Method for Predicting the Remaining Useful Life of Bearings
MachinesMaintaining 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 +4 more sources
Prediction of remaining useful life (RUL) of Komatsu excavator under reliability analysis in the Weibull-frailty model. [PDF]
PLoS One, 2020 Es una tarea esencial estimar la vida útil restante (RUL) de la maquinaria en el sector minero destinada a garantizar la producción y la satisfacción del cliente. En este estudio, se utilizó un marco conceptual para determinar el RUL bajo el análisis de confiabilidad en un modelo de fragilidad. El marco propuesto se implementó en una excavadora Komatsu
Ghomghaleh A +6 more
europepmc +5 more sources
Hybrid framework for Remaining Useful Life (RUL) prediction of rolling bearing faults
ArrayRemaining Useful Life (RUL) prediction is critical for preventing catastrophic failures in industrial systems, enabling efficient maintenance scheduling and resource optimization.
Ali Saeed +6 more
doaj +2 more sources
Remaining useful life (RUL) regression using Long–Short Term Memory (LSTM) networks [PDF]
Microelectronics Reliability, 2022 Sofia Yousuf, Salman A Khan
exaly +2 more sources
A Real-Time Diagnostic System Using a Long Short-Term Memory Model with Signal Reshaping Technology for Ship Propellers [PDF]
SensorsThis study develops a ship propeller diagnostic system to address the issue of insufficient ship maintenance capacity and enhance operational efficiency.
Sheng-Chih Shen +4 more
doaj +2 more sources
Ensemble Neural Networks for Remaining Useful Life (RUL) Prediction
PHM Society Asia-Pacific Conference, 2023 A core part of maintenance planning is a monitoring system that provides a good prognosis on health and degradation, often expressed as remaining useful life (RUL). Most of the current data-driven approaches for RUL prediction focus on single-point prediction.
Abhishek Srinivasan +2 more
openaire +2 more sources
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