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 +4 more sources
Ensemble Neural Networks for Remaining Useful Life (RUL) Prediction [PDF]
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 +3 more sources
Prediction of Remaining Useful Life (RUL) of Lithium ion (Li-ion) Batteries [PDF]
, 2020 In recent time Li-ion battery gained popularity because of their high charge density, portability and longer life span. It compliments the human quest for green energy. As many of the green energy applications like Electrical Vehicle, Wind Energy and Solar Energy use Li-ion battery as their energy storage device.
Shukla, Rashmikant T.
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Prognostics for electronics components of avionics - NASA IGBT accelerated ageing case study [PDF]
, 2013 Insulate gate bipolar transistors (IGBTs) are widely used in electric vehicles, railway locomotive and new generation aircrafts, due to the IGBTs have advantages in small conduction resistance and small drive current.
Xie, Yuan'an
core +7 more sources
jiaxiang-cheng/cnn-pytorch-remaining-useful-life-prediction: RUL Prediction with CNN
, 2021 Inspired by Babu, G. S., Zhao, P., & Li, X. L. (2016, April). Deep convolutional neural network-based regression approach for estimation of remaining useful life. In International conference on database systems for advanced applications (pp.
Jia-Xiang Cheng
core +1 more source
A Semi-Supervised Approach with Monotonic Constraints for Improved Remaining Useful Life Estimation
Sensors, 2022 Remaining useful life is of great value in the industry and is a key component of Prognostics and Health Management (PHM) in the context of the Predictive Maintenance (PdM) strategy.
Diego Nieves Avendano +6 more
doaj +1 more source
Machine performance degradation assessment and remaining useful life prediction using proportional hazard model and support vector machine [PDF]
, 2012 Machine performance degradation assessment and remaining useful life (RUL) prediction are of crucial importance in condition-based maintenance to reduce the maintenance cost and improve the reliability.
Yang, Bo-Suk +3 more
core +1 more source
Semi-Supervised Framework with Autoencoder-Based Neural Networks for Fault Prognosis
Sensors, 2022 This paper presents a generic framework for fault prognosis using autoencoder-based deep learning methods. The proposed approach relies upon a semi-supervised extrapolation of autoencoder reconstruction errors, which can deal with the unbalanced ...
Tiago Gaspar da Rosa +5 more
doaj +1 more source
Machine performance degradation assessment and remaining useful life prediction using proportional hazard model and SVM [PDF]
This paper proposes a three-stage method involved system identification techniques, proportional hazard model, and support vector machine for assessing the machine health degradation and forecasting the machine remaining useful life (RUL).
Tran, Van Tung
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Demonstration of a Response Time Based Remaining Useful Life (RUL) Prediction for Software Systems
CoRR, 2023 Prognostic and Health Management (PHM) has been widely applied to hardware systems in the electronics and non-electronics domains but has not been explored for software. While software does not decay over time, it can degrade over release cycles. Software health management is confined to diagnostic assessments that identify problems, whereas prognostic
Ray Islam, Peter Sandborn
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