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Lookup NU author(s): Genevieve Moat, Dr Shirley ColemanORCiD
This is the authors' accepted manuscript of a conference proceedings (inc. abstract) that has been published in its final definitive form by IEEE, 2022.
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Maintenance is a critical component of Facilities Management (FM), and with the proliferation of big data, Internet of Things (IoT), and Industry 4.0, predictive maintenance (PdM) has emerged as a critical maintenance technique. However, modern data-driven PdM tactics are based on sensor data, but there is no obvious way to imply PdM on older buildings that lack sensors. EQUANS is a company seeking recommendations for implying PdM in the management of a historic building. This paper demonstrates the potential of survival analysis with data-driven PdM using EQUANS’s non-sensored data, explicitly using the Kaplan-Meier method, parametric methods, Cox proportional hazard model, and accelerated failure time models. The boiler was chosen as the asset to focus on in this project, and the results indicated that the boiler’s survival might not be related to the frequency of service but the boiler’s age. The research findings propose a further step toward PdM for assets without sensors, and data collection and preventive maintenance can be improved.
Author(s): Moat G, Coleman S
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: IEEE International Conference on Big Data (Big Data)
Year of Conference: 2022
Pages: 4026-4034
Print publication date: 13/01/2022
Online publication date: 13/01/2022
Acceptance date: 21/11/2021
Date deposited: 25/01/2022
Publisher: IEEE
URL: https://doi.org/10.1109/BigData52589.2021.9671625
DOI: 10.1109/BigData52589.2021.9671625
Library holdings: Search Newcastle University Library for this item
ISBN: 9781665439022