Adopting machine learning and condition monitoring P-F curves in determining and prioritizing high-value assets for life extension

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dc.contributor.authorOchella, Sunday
dc.contributor.authorShafiee, Mahmood
dc.contributor.authorSansom, Christopher L.
dc.date.accessioned2021-04-16T09:59:03Z
dc.date.available2021-04-16T09:59:03Z
dc.date.issued2021-03-13
dc.description.abstractMany machine learning algorithms and models have been proposed in the literature for predicting the remaining useful life (RUL) of systems and components that are subject to condition monitoring (CM). However, in cases where data is ubiquitous, identifying the most suitable equipment for life-extension based on CM data and RUL predictions is a rather challenging task. This paper proposes a technique for determining and prioritizing high-value assets for life-extension treatments when they reach the end of their useful life. The technique exploits the use of key concepts in machine learning (such as data mining and k-means clustering) in combination with an important tool from reliability-centered maintenance (RCM) called the potential-failure (P-F) curve. The RCM process identifies essential equipment within a plant which are worth monitoring, and then derives the P-F curves for equipment using CM and operational data. Afterwards, a new index called the potential failure interval factor (PFIF) is calculated for each equipment or unit, serving as a health indicator. Subsequently, the units are grouped in two ways: (i) a regression model in combination with suitably defined PFIF window boundaries, (ii) a k-means clustering algorithm based on equipment with similar data features. The most suitable equipment for life-extension are identified in groups in order to aid in planning, decision-making and deployment of maintenance resources. Finally, the technique is empirically tested on NASA’s Commercial Modular Aero-Propulsion System Simulation datasets and the results are discussed in detail.en_UK
dc.identifier.citationOchella S, Shafiee M, Sansom C. (2021) Adopting machine learning and condition monitoring P-F curves in determining and prioritizing high-value assets for life extension. Expert Systems with Applications, Volume 176, August 2021, Article number 114897en_UK
dc.identifier.issn0957-4174
dc.identifier.urihttps://doi.org/10.1016/j.eswa.2021.114897
dc.identifier.urihttp://dspace.lib.cranfield.ac.uk/handle/1826/16585
dc.language.isoenen_UK
dc.publisherElsevieren_UK
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectCondition monitoringen_UK
dc.subjectRemaining useful lifeen_UK
dc.subjectLife-extensionen_UK
dc.subjectK-means clusteringen_UK
dc.subjectPotential failure interval factoren_UK
dc.titleAdopting machine learning and condition monitoring P-F curves in determining and prioritizing high-value assets for life extensionen_UK
dc.typeArticleen_UK

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