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Lookup NU author(s): Dr Manuel HerreraORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© IMechE 2026. This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage). Telecommunication base station monitoring systems trigger alarms in response to network events, degradations, and recoveries. These alarm sequences are crucial for predictive analytics and for quantifying resilience at base stations and across the entire network. Alarms often hide structured information flows that can be revealed by exploring their causality, thereby revealing the dynamism and interdependencies among the infrastructure’s network components. The RAPTOR framework proposed in this work uses temporal clustering, first-order Markov modelling, and resilience metrics to analyse alarm logs and predict alarm behaviour in Radio Access Network (RAN) base stations. Using Dynamic Time Warping (DTW) to identify co-activated alarm clusters and Markov transition matrices to estimate alarm propagation, the proposed framework supports accurate modelling of information flow, next-alarm prediction, and interpretable resilience scores based on alarm entropy, self-loop tendency, and absorption probability. This framework is developed and validated on real-world alarm logs from numerous base stations of a major telecommunication service provider in the United Kingdom. Preliminary evaluation results indicate this approach is a good resource for network managers in infrastructure planning and maintenance scheduling, utilising features such as early warning, causality tracing, root-cause insights, and alarm interpretability.
Author(s): Mukherjee A, Chandra A, Herrera M, Li L, Indiran HP, Parekh A, Parlikad AK
Publication type: Article
Publication status: Published
Journal: Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability
Year: 2026
Pages: Epub ahead of print
Online publication date: 26/03/2026
Acceptance date: 09/02/2026
Date deposited: 14/04/2026
ISSN (print): 1748-006X
ISSN (electronic): 1748-0078
Publisher: SAGE Publications Ltd
URL: https://doi.org/10.1177/1748006X261431895
DOI: 10.1177/1748006X261431895
Data Access Statement: The data underlying this study are proprietary to the collaborating industry partner and cannot be shared publicly due to confidentiality and contractual restrictions. Derived, de-identified, or aggregated results may be available from the corresponding author upon reasonable request and subject to the partner’s approval.
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