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Ensemble Learning for Remaining Useful Life Prediction
PHM Society Asia-Pacific Conference, 2017While significant research has been conducted in modelbased and data-driven prognostics, very limited research has been done to investigate the prediction of RUL using an ensemble learning method that combines prediction results from multiple learning algorithms.
Zhixiong Li, Chao Hu
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Prediction of Mining Railcar Remaining Useful Life
2019Railcar or Rolling stock that is referring to all vehicles moving on railway, is one of the most important component of the rail transport system. In-operation failures of the railcar results delays in transportation and therefore predicting the remaining useful life (RUL) and accordingly considering the preventive maintenance activities are essential.
Mohammad Javad Rahimdel +2 more
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Remaining Useful Life Prediction Based on Spatiotemporal Autoencoder
SSRN Electronic Journal, 2022Tao Xu, Dechang Pi, Shi Zeng
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A Remaining Useful Life Prediction Framework for Multi-sensor System
2019 IEEE 19th International Conference on Software Quality, Reliability and Security Companion (QRS-C), 2019As the key technology of prognostics and health management, the remaining useful life (RUL) prediction can effectively reduce the fault probability and maintenance cost by evaluating the system operation condition. Due to the complex structure and versatility, an engineering system requires multiple sensors to monitor its condition.
Heng Zhang 0036 +3 more
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Remaining Useful Life Prediction of Equipment Based on XGBoost
Proceedings of the 5th International Conference on Computer Science and Application Engineering, 2021Zhiyang Jia, Zhibo Xiao, Yijin Shi
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The Study of Hyperparameters in the Prediction of Remaining Useful Life
Volume 11: Safety Engineering, Risk and Reliability Analysis; Research PostersAbstract Despite the extensive body of literature on RUL prediction techniques, there is still a need to understand how hyperparameters affect these predictions’ error and aleatoric and epistemic uncertainty. Motivated by this need, the research question was: Does applying machine learning architecture enhance uncertainty quantification ...
Nazir Laureano Gandur +1 more
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Domain Adaptive Remaining Useful Life Prediction With Transformer
IEEE Transactions on Instrumentation and Measurement, 2022Xinyao Li +4 more
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Predicting Remaining Useful Life of Well Tubulars
Ibero-Latin American Congress on Computational Methods in Engineering (CILAMCE)This work proposes a reliability-based framework to estimate the Remaining Useful Life (RUL) of well tubulars throughout the lifecycle of oil and gas wells. The RUL estimation is supported by a probabilistic analysis that accounts for uncertainties in geometrical and material properties, based on failure modes defined in the API/TR 5C3 standard.
null Luís Philipe Ribeiro Almeida +6 more
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Predicting Remaining Useful Life Using AdaBoost Algorithm
Advances in Science and TechnologyPredicting the Remaining Useful Life (RUL) of machinery and critical components is crucial for proactive maintenance and operational efficiency in industrial settings. This paper presents an approach to RUL prediction using the AdaBoost algorithm, a technique that iteratively improves prediction accuracy by focusing on difficult-to-predict cases.
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Remaining Useful Life Prediction for Aircraft Maintenance Using Machine Learning
Quality and Reliability Engineering InternationalABSTRACT Ensuring regular equipment maintenance is critical for any business that relies on machinery. Predictive maintenance (PdM) is a strategy for scheduling maintenance tasks, with a primary focus on predicting the remaining useful life (RUL) of equipment in advance.
Sana Abbes +4 more
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