A Decision-Based Framework for Predictive Maintenance Technique Selection in Industry 4.0



Li, J, Schaefer, D ORCID: 0000-0002-5695-9312 and Milisavljevic-Syed, J
(2022) A Decision-Based Framework for Predictive Maintenance Technique Selection in Industry 4.0. Procedia CIRP, 107. pp. 77-82.

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Abstract

Maintenance is defined as the actions that allow machines and equipment to work for an extended period of time by retaining and restoring equipment to its original state. In Industry 4.0 context, Predictive Maintenance (PdM) is a strategy that utilizes digitized sensor data and data analytics to continuously monitor the state of machine components or processes to determine when and where maintenance actions may be required. There are five key types of PdM techniques being used in practice: experience-based, model-based, physical-based; data-driven; and hybrid. Selecting the most suitable PdM technique for a given setup or scenario is critical for any successful PdM implementation in industry to optimize cost and time. To help businesses in identifying and selecting the most appropriate PdM technique for their specific purposes, the authors propose a corresponding decision-making framework based on several critical factors to be considered in the process. They also discuss how the framework might best be used in industrial strategic planning processes and elaborate on its limitations and challenges.

Item Type: Article
Divisions: Faculty of Science and Engineering > School of Engineering
Depositing User: Symplectic Admin
Date Deposited: 06 Jun 2022 08:55
Last Modified: 18 Jan 2023 21:00
DOI: 10.1016/j.procir.2022.04.013
Open Access URL: https://www.sciencedirect.com/science/article/pii/...
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URI: https://livrepository.liverpool.ac.uk/id/eprint/3155920