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An RUL-Informed Approach for Life Extension of High-Value Assets

Ochella, Sunday, Shafiee, Mahmood, Sansom, Chris (2022) An RUL-Informed Approach for Life Extension of High-Value Assets. Computers and Industrial Engineering, 171 . Article Number 108332. ISSN 0360-8352. (doi:10.1016/j.cie.2022.108332) (KAR id:95730)

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https://doi.org/10.1016/j.cie.2022.108332

Abstract

The conventional approaches for life-extension (LE) of industrial assets are largely qualitative and focus only on a few indicators at the end of an asset’s design life. However, an asset may consist of numerous individual components with different useful lives and therefore applying a single LE strategy to every component will not result in an efficient outcome. In recent years, many advanced analytics techniques have been proposed to estimate the remaining useful life (RUL) of the assets equipped with sensor technology. This paper proposes a data-driven model for LE decision-making based on RUL values predicted on a real-time basis during the asset’s operational life. Our proposed LE model is conceptually targeted at the component, unit, or subsystem level; however, an asset-level decision is made by aggregating information across all components. Consequently, LE is viewed and assessed as a series of ongoing activities, albeit carefully orchestrated in a manner similar to operation and maintenance (O&M). The application of the model is demonstrated using the publicly available NASA C-MAPSS dataset for large commercial turbofan engines. This approach will be very beneficial to asset owners and maintenance engineers as it seamlessly weaves LE strategies into O&M activities, thus optimizing resources.

Item Type: Article
DOI/Identification number: 10.1016/j.cie.2022.108332
Uncontrolled keywords: Remaining useful life (RUL); Life extension (LE); Prognostics and health management (PHM); Machine learning (ML); Reliability centered maintenance (RCM); Turbofan engines
Subjects: T Technology > TA Engineering (General). Civil engineering (General) > TA401 Materials engineering and construction
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: 08 Jul 2022 18:20 UTC
Last Modified: 13 Jan 2024 13:35 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/95730 (The current URI for this page, for reference purposes)

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