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Survival Analysis and Predictive Maintenance Models for non-sensored Assets in Facilities Management

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.

Publication metadata

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


DOI: 10.1109/BigData52589.2021.9671625

Library holdings: Search Newcastle University Library for this item

ISBN: 9781665439022