Evaluation of Smart Sensors for Subway Electric Motor Escalators through AHP-Gaussian Method
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 sa...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/8/4131 |
| _version_ | 1797603541161869312 |
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| author | Ruan Carlos Alves Pereira Orivalde Soares da Silva Renata Albergaria de Mello Bandeira Marcos dos Santos Claudio de Souza Rocha Cristian dos Santos Castillo Carlos Francisco Simões Gomes Daniel Augusto de Moura Pereira Fernando Martins Muradas |
| author_facet | Ruan Carlos Alves Pereira Orivalde Soares da Silva Renata Albergaria de Mello Bandeira Marcos dos Santos Claudio de Souza Rocha Cristian dos Santos Castillo Carlos Francisco Simões Gomes Daniel Augusto de Moura Pereira Fernando Martins Muradas |
| author_sort | Ruan Carlos Alves Pereira |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T04:33:35Z |
| format | Article |
| id | doaj.art-fdeedd3cbfda4893a4f7d0953d186acb |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T04:33:35Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-fdeedd3cbfda4893a4f7d0953d186acb2023-11-17T21:19:34ZengMDPI AGSensors1424-82202023-04-01238413110.3390/s23084131Evaluation of Smart Sensors for Subway Electric Motor Escalators through AHP-Gaussian MethodRuan Carlos Alves Pereira0Orivalde Soares da Silva1Renata Albergaria de Mello Bandeira2Marcos dos Santos3Claudio de Souza Rocha4Cristian dos Santos Castillo5Carlos Francisco Simões Gomes6Daniel Augusto de Moura Pereira7Fernando Martins Muradas8Military Engineering Institute—IME, Rio de Janeiro 22290-270, BrazilMilitary Engineering Institute—IME, Rio de Janeiro 22290-270, BrazilMilitary Engineering Institute—IME, Rio de Janeiro 22290-270, BrazilDepartment of Production Engineering, Faculty of Engineering, Praia Vermelha Campus, Federal Fluminense University, Niteroi 22040-036, BrazilDepartment of Production Engineering, Faculty of Engineering, Praia Vermelha Campus, Federal Fluminense University, Niteroi 22040-036, BrazilDepartment of Production Engineering, Faculty of Engineering, Praia Vermelha Campus, Federal Fluminense University, Niteroi 22040-036, BrazilDepartment of Production Engineering, Faculty of Engineering, Praia Vermelha Campus, Federal Fluminense University, Niteroi 22040-036, BrazilProduction Engineering Department, Federal University of Campina Grande (UFCG), Campina Grande 58428-830, BrazilOperational Research Department, Naval Systems Analysis Center (CASNAV), Rio de Janeiro 20091-000, BrazilThis 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.https://www.mdpi.com/1424-8220/23/8/4131decision making4.0 industryautomationoperational researchsubwaypredictive maintenance |
| spellingShingle | Ruan Carlos Alves Pereira Orivalde Soares da Silva Renata Albergaria de Mello Bandeira Marcos dos Santos Claudio de Souza Rocha Cristian dos Santos Castillo Carlos Francisco Simões Gomes Daniel Augusto de Moura Pereira Fernando Martins Muradas Evaluation of Smart Sensors for Subway Electric Motor Escalators through AHP-Gaussian Method Sensors decision making 4.0 industry automation operational research subway predictive maintenance |
| title | Evaluation of Smart Sensors for Subway Electric Motor Escalators through AHP-Gaussian Method |
| title_full | Evaluation of Smart Sensors for Subway Electric Motor Escalators through AHP-Gaussian Method |
| title_fullStr | Evaluation of Smart Sensors for Subway Electric Motor Escalators through AHP-Gaussian Method |
| title_full_unstemmed | Evaluation of Smart Sensors for Subway Electric Motor Escalators through AHP-Gaussian Method |
| title_short | Evaluation of Smart Sensors for Subway Electric Motor Escalators through AHP-Gaussian Method |
| title_sort | evaluation of smart sensors for subway electric motor escalators through ahp gaussian method |
| topic | decision making 4.0 industry automation operational research subway predictive maintenance |
| url | https://www.mdpi.com/1424-8220/23/8/4131 |
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