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The Strathprints institutional repository is a digital archive of University of Strathclyde's Open Access research outputs. Strathprints provides access to thousands of Open Access research papers by Strathclyde researchers, including by researchers from the Physical Activity for Health Group based within the School of Psychological Sciences & Health. Research here seeks to better understand how and why physical activity improves health, gain a better understanding of the amount, intensity, and type of physical activity needed for health benefits, and evaluate the effect of interventions to promote physical activity.

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Predicting remaining life of transmission tower steelwork components

Segovia, M. and Catterson, V. M. and Stuart, A. and Johnston, L. and Bain, H. and McPhaden, R. and Wylie, R. and Hernandez, A. (2016) Predicting remaining life of transmission tower steelwork components. In: Risk, Reliability and Safety. CRC Press, London, UK. ISBN 9781138029972

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Segovia_etal_ESREL2016_Predicting_remaining_life_of_transmission_tower_steelwork_components.pdf - Accepted Author Manuscript
Restricted to Repository staff only until 13 September 2017.

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Abstract

Failures in transmission tower’s components usually result in extended disruption of power supply. Repair is very costly as it involves replacement of the transmission lines’ sections affected. Additionally, it might also entail litigation cost associated with power disruption. Maintenance decisions have to be taken in time to prevent a failure. At present, maintenance decisions are mainly based on expert’s judgement, who perform inspections every 10 to 12 years. On specific sites, tower’s components degrade much faster due to aggressive atmospheric conditions, with corrosion being the primary cause of deterioration. In this context, data indicating health state from an UK utility were used to create a Cox model that relates the time before a failure occurs to climatic and atmospheric conditions highly correlated with corrosion. The paper demonstrates the use of the model for predicting remaining tower life, and highlights how this can feed into maintenance planning.