Contributions of the Expert Systems and Machine Learning Techniques at the Prescriptive Maintenance Level
Year: 2024
Type: conference-paper
Abstract: Purpose – This paper aims to present the main factors necessary for the implementation of machine learning techniques applied to intelligent systems for the development of prescriptive maintenance systems. Methodology – Expert systems and machine learning systems have the capacity for ongoing updates and enhancements, enabling organizations to fine-tune their maintenance approaches in response to changing insights and feedback. Industry 4.0 has brought significant contributions in terms of re-engineering prescriptive maintenance systems. Findings – Expert systems and machine learning techniques complement each other in prescriptive maintenance by leveraging historical data, real-time monitoring and adaptive decision-making to enhance equipment reliability, minimize downtime and optimize maintenance strategies. Research implications – Both systems can effortlessly incorporate Internet of Things sensors for the acquisition of real-time data, improving the precision and promptness of forecasts and suggestions. Practical implications – Automated expert systems enhance efficiency, precision and prompt responsiveness in maintenance operations through the application of predetermined rules, analysis of data and decision-making, all accomplished without the need for manual input at each stage. Originality – This article highlights the significant contributions of expert systems in the context of prescriptive maintenance activities of organizations.
Authors: POP-SUĂRĂȘAN, Ana-Diana, UNGUREANU, Nicolae Stelian
Institution: Technical University of Cluj-Napoca
Language: English
Subfield: Safety, Risk, Reliability and Quality
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6930545375
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