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
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
Predicting Remaining Useful Life using Time Series Embeddings based on Recurrent Neural Networks [PDF]
, 2017 We consider the problem of estimating the remaining useful life (RUL) of a system or a machine from sensor data. Many approaches for RUL estimation based on sensor data make assumptions about how machines degrade.
Agarwal, Puneet +5 more
core +3 more sources
A Data-Driven Fuzzy Approach for Predicting the Remaining Useful Life in Dynamic Failure Scenarios of a Nuclear Power Plant [PDF]
, 2010 This paper presents a similarity-based approach for prognostics of the Remaining Useful Life (RUL) of a system, i.e. the lifetime remaining between the present and the instance when the system can no longer perform its function. Data from failure dynamic
Di Maio, F., Zio, Enrico
core +3 more sources
A Novel Approach of Label Construction for Predicting Remaining Useful Life of Machinery
Shock and Vibration, 2021 Rolling bearings are key components of rotating machinery, and predicting the remaining useful life (RUL) is of great significance in practical industrial scenarios and is being increasingly studied.
Hailong Lin +7 more
doaj +1 more source
Lithium-ion batteries Remaining Useful Life Prediction Method Considering Recovery Phenomenon
International Journal of Electrochemical Science, 2019 Estimation of lithium battery remaining useful life (RUL) is the key to lithium battery health management. In the process of intermittent discharging lithium batteries, the recovery phenomenon will have a relatively large impact on the life of lithium ...
Zhenyu Zhang +5 more
doaj +1 more source
A Transfer Learning-based Approach to Predict the Shelf life of Fruit
Inteligencia Artificial, 2021 Shelf-life prediction for fruits based on the visual inspection and with RGB imaging through external features becomes more pervasive in agriculture and food business.
Varsha Bhole, Arun Kumar
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Remaining Useful Life Prediction for Lithium-Ion Battery: A Deep Learning Approach
IEEE Access, 2018 Accurate prediction of remaining useful life (RUL) of lithium-ion battery plays an increasingly crucial role in the intelligent battery health management systems. The advances in deep learning introduce new data-driven approaches to this problem.
Lei Ren +5 more
doaj +1 more source
IEEE Access, 2020 Remaining useful life (RUL) estimation is expected to provide appropriate maintenance for components or systems in industry to improve the reliability of the systems.
Fei Li +8 more
doaj +1 more source
RUL Prediction of Rolling Bearings Based on a DCAE and CNN
Applied Sciences, 2021 Predicting the remaining useful life (RUL) of mechanical equipment can improve production efficiency while effectively reducing the life cycle cost and failure rate.
Chenyang Wang +3 more
doaj +1 more source