Scientific research in the area of fault prognosis is increasingly focused on estimating the Remaining Useful Life of equipment, since its knowledge is a key input to the scheduling of Condition-Based and Predictive Maintenance. Several research studies have been directed to developing methods for modeling the trend of health indicators for Remaining Useful Life estimation, this paper makes a review of these approaches. Fault diagnosis methods sensitive to the progressive evolution of degradation phenomena are presented and their usability for fault prognosis is discussed. Then, methods for modeling the trends of health indicators are analyzed to highlight the selection criteria of the modeling methods, according to the available information on the operating conditions of the systems, and on the degradation phenomenon. Finally, some reflections are made regarding the elements that prevent the large-scale use of prognostics in industry today, and on the integration of prognostics in risk assessment and management.

Review on health indices extraction and trend modeling for remaining useful life estimation

Zio E.
2020

Abstract

Scientific research in the area of fault prognosis is increasingly focused on estimating the Remaining Useful Life of equipment, since its knowledge is a key input to the scheduling of Condition-Based and Predictive Maintenance. Several research studies have been directed to developing methods for modeling the trend of health indicators for Remaining Useful Life estimation, this paper makes a review of these approaches. Fault diagnosis methods sensitive to the progressive evolution of degradation phenomena are presented and their usability for fault prognosis is discussed. Then, methods for modeling the trends of health indicators are analyzed to highlight the selection criteria of the modeling methods, according to the available information on the operating conditions of the systems, and on the degradation phenomenon. Finally, some reflections are made regarding the elements that prevent the large-scale use of prognostics in industry today, and on the integration of prognostics in risk assessment and management.
Artificial Intelligence Techniques for a Scalable Energy Transition: Advanced Methods, Digital Technologies, Decision Support Tools, and Applications
978-3-030-42725-2
978-3-030-42726-9
Data-driven approaches
Fault diagnosis
Fault prognosis
Model-based approaches
Remaining useful life
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11311/1160939
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