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Adopting Machine Learning and Condition Monitoring P-F Curves in Determining and Prioritizing High-Value Assets for Life Extension

Ochella, Sunday, Shafiee, Mahmood, 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, 176 . Article Number 114897. ISSN 0957-4174. (doi:10.1016/j.eswa.2021.114897) (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided) (KAR id:87392)

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https://doi.org/10.1016/j.eswa.2021.114897

Abstract

Many 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.

Item Type: Article
DOI/Identification number: 10.1016/j.eswa.2021.114897
Uncontrolled keywords: Machine learning; Data mining; Potential failure interval factor; K-means clustering; Life-extension; Remaining useful life; Condition monitoring
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming, > QA76.76.E95 Expert Systems (Intelligent Knowledge Based Systems)
T Technology > TJ Mechanical engineering and machinery
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts
Depositing User: Mahmood Shafiee
Date Deposited: 31 Mar 2021 07:09 UTC
Last Modified: 01 Apr 2021 09:10 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/87392 (The current URI for this page, for reference purposes)
Shafiee, Mahmood: https://orcid.org/0000-0002-6122-5719
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