Artículo

Evaluation of Smart Sensors for Subway Electric Motor Escalators through AHP-Gaussian Method

Resumen

This paper proposes the use of the AHP-Gaussian method to support the selection of a smart sensor installation for an electric motor used in an escalator in a subway station. The AHP-Gaussian methodology utilizes the Analytic Hierarchy Process (AHP) framework and is highlighted for its ability to save the decision maker’s cognitive effort in assigning weights to criteria. Seven criteria were defined for the sensor selection: temperature range, vibration range, weight, communication distance, maximum electric power, data traffic speed, and acquisition cost. Four smart sensors were considered as alternatives. The results of the analysis showed that the most appropriate sensor was the ABB Ability smart sensor, which scored the highest in the AHP-Gaussian analysis. In addition, this sensor could detect any abnormalities in the equipment’s operation, enabling timely maintenance and preventing potential failures. The proposed AHP-Gaussian method proved to be an effective approach for selecting a smart sensor for an electric motor used in an escalator in a subway station. The selected sensor was reliable, accurate, and cost-effective, contributing to the safe and efficient operation of the equipment.
Khamisy-Farah, Rola (55808741800); Gilbey, Peter (55980203000); Furstenau, Leonardo B. (57211463471); Sott, Michele Kremer (57218374403); Farah, Raymond (14013894800); Viviani, Maurizio (57231700200); Bisogni, Maurizio (57232852500); Kong, Jude Dzevela (56305065700); Ciliberti, Rosagemma (6507895664); Bragazzi, Nicola Luigi (57212030091)
Big data for biomedical education with a focus on the covid-19 era: An integrative review of the literature
2021
10.3390/ijerph18178989
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113546517&doi=10.3390%2fijerph18178989&partnerID=40&md5=d9dcc718aa0b917324d63a4b1f8d88c9
Clalit Health Service, Akko, Azrieli Faculty of Medicine, Bar-Ilan University, Safed, 13100, Israel; Azrieli Faculty of medicine, Bar Ilan University, Safed, 13100, Israel; Department of Industrial Engineering, Federal University of Rio Grande do Sul, Porto Alegre, 90035-190, Brazil; Business School, Unisinos University, Porto Alegre, 91330-002, Brazil; Department of Internal Medicine B, Ziv Medical Center, Azrieli Faculty of Medicine, Bar-Ilan University, Safed, 13100, Israel; TransHumanGene, MedicaSwiss, Cham, Zug, 6330, Switzerland; Laboratory for Industrial and Applied Mathematics (LIAM), Department of Mathematics and Statistics, York University, Toronto, M3J 1P3, ON, Canada; Section of History of Medicine and Bioethics, Department of Health Sciences (DISSAL), University of Genoa, Genoa, 16132, Italy
All Open Access; Gold Open Access; Green Open Access
Scopus
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