At the recent ETFA 2024 Conference held in Padova, Italy, Ioanna Marina Anagnostara, Research Associate at CERTH, represented ECOFACT during the session TT4 – New Methods for Diagnosis and Fault Analysis. Her presentation, titled “Cost-Oriented Predictive Maintenance using Exponential Degradation Modelling: Application on Manufacturing Industries,” focused on ECOFACT’s innovative work in Predictive Maintenance (PdM), specifically highlighting the PdM tool developed within Task 3.2 on energy simulation for dynamic operations management and cost optimisation, led by CERTH.
In today’s Industry 4.0 landscape, characterised by the seamless integration of Internet of Things (IoT) and advanced analytics, PdM stands out as a transformative strategy. Unlike traditional reactive maintenance approaches, PdM monitors equipment in real-time to execute maintenance actions precisely when needed, thereby avoiding unnecessary preventive measures and unexpected failures.
Anagnostara’s presentation delved into the development of a comprehensive PdM methodology that integrates Remaining Useful Life (RUL) estimation and economic performance assessment within a novel framework. Central to this approach is an evolutionary algorithm designed to model and optimise maintenance schedules based on exponential degradation patterns. This methodological innovation includes a RUL-based decision support tool, which identifies the optimal timing for maintenance activities.
The practical application of this research was exemplified through its implementation on the CO2 evaporator at Athenian Brewery, showcasing real-world effectiveness within an operational manufacturing system (learn more about the Athenian Brewery demonstration case diving in this webinar). The study’s findings underscore the significance of intelligent, cost-oriented PdM strategies in enhancing operational efficiency, reducing downtime, and optimising industrial costs.
Anagnostara’s presentation was well-received for its contributions to advancing industrial maintenance practices, as detailed in the paper. You read it in full here.
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