Fundamentals of Prognostics

Bregón, Aníbal; Daigle, Matthew

doi:10.1007/978-3-030-17728-7_17

Cited by 3 publications

(1 citation statement)

References 33 publications

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“…As in the case of consumption prediction, prognosis has been implemented using approaches that are data-driven, physics-based, or hybrid approaches [27]. During prognosis and health-management (PHM) analysis, historical knowledge carried by data is systematically fed into a decision-making process by sophisticated data modelling techniques [28].…”

Section: Related Workmentioning

confidence: 99%

Sensor-Data-Driven Prognosis Approach of Liquefied Natural Gas Satellite Plant

Escobet

Quevedo

et al. 2020

ASI

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This paper proposes a sensor-data-driven prognosis approach for the predictive maintenance of a liquefied natural gas (LNG) satellite plant. By using data analytics of sensors installed in the satellite plants, it is possible to predict the remaining time to refill the tank of the remote plants. In the proposed approach, the first task of data validation and correction is presented in order to transform raw data into reliable validated data. Then, the second task presents two methods for the prognosis of gas consumption in real time and the forecast of remaining time to refill the tank of the plant. The obtained results with real satellite plants showed good performance for direct implementation in a predictive maintenance plan.

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Section: Related Workmentioning

confidence: 99%

Sensor-Data-Driven Prognosis Approach of Liquefied Natural Gas Satellite Plant

Escobet

Quevedo

et al. 2020

ASI

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Computational adaptive multivariable degradation model for improving the remaining useful life prediction in industrial systems

et al. 2022

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Artificial Intelligence-Driven Prognostics and Health Management for Centrifugal Pumps: A Comprehensive Review

Khalid,

Jo,

Shah

et al. 2024

Actuators

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This comprehensive review explores data-driven methodologies that facilitate the prognostics and health management (PHM) of centrifugal pumps (CPs) while utilizing both vibration and non-vibration sensor data. This review investigates common fault types in CPs, while placing a specific emphasis on artificial intelligence (AI) approaches, including machine learning (ML) and deep learning (DL) techniques, for fault diagnosis and prognosis. A key innovation of this review is its in-depth analysis of cutting-edge methods, such as adaptive thresholding, hybrid models, and advanced neural network architectures, aimed at accurately predicting the remaining useful life (RUL) of CPs under varying operational conditions. This review also addresses the limitations and challenges of the current AI-driven methodologies, offering insights into potential solutions. By synthesizing these methodologies and presenting practical applications through case studies, this review provides a forward-looking perspective to empower industry professionals and researchers with effective strategies to ensure the reliability and efficiency of centrifugal pumps. These findings could contribute to optimizing industrial processes and advancing health management strategies for critical components.

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Fundamentals of Prognostics

Cited by 3 publications

References 33 publications

Sensor-Data-Driven Prognosis Approach of Liquefied Natural Gas Satellite Plant

Sensor-Data-Driven Prognosis Approach of Liquefied Natural Gas Satellite Plant

Computational adaptive multivariable degradation model for improving the remaining useful life prediction in industrial systems

Artificial Intelligence-Driven Prognostics and Health Management for Centrifugal Pumps: A Comprehensive Review

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