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

Li, J.; Schaefer, D.; Milisavljevic-Syed, J.

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.

Citation

Li, J., Schaefer, D., & Milisavljevic-Syed, J. A Decision-Based Framework for Predictive Maintenance Technique Selection in Industry 4.0. Presented at 55th CIRP Conference on Manufacturing Systems 2022, Lugano, Switzerland

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 55th CIRP Conference on Manufacturing Systems 2022
• Acceptance Date: May 26, 2022
• Publication Date: Jan 1, 2022
• Deposit Date: Apr 3, 2024
• Publicly Available Date: Apr 5, 2024
• Journal: Procedia CIRP
• Print ISSN: 2212-8271
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 107
• Pages: 77-82
• DOI: https://doi.org/10.1016/j.procir.2022.04.013
• Public URL: https://hull-repository.worktribe.com/output/4618502