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

Abstract

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.

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“…This is supported by the lowest total error value compared to the other configurations, except for ‘ e ’, which, despite having the lowest total error, received a lower ranking due to its error distribution across various dimensions. This multivariate evaluation approach is supported by Dos Dantos et al, Pereira et al, and Rodrigues et al [ 26 , 29 , 30 ] in the AHP-G method, emphasizing the importance of considering multiple criteria in decision-making processes. Furthermore, the Gaussian adaptation employed here follows the logic presented by Buckley [31] , suggesting the inclusion of probability distributions to capture the uncertainty and variability in decision-maker preferences.…”

“…This notion is supported by Wong and Li [32] , who highlight the relevance of standard deviation in data normalization for AHP analyses, allowing for a more robust evaluation of options. By integrating these metrics, the Gaussian AHP method provides a comprehensive and mathematically sound approach for determining the optimal configuration among multiple options with various error dimensions [ 26 , 29 , 30 ].…”

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“…It has the unique capability to distill the insights and intuitive judgments of experienced professionals into tangible data that can guide pivotal decisions. Since its inception in the 1970s, the AHP has become an indispensable tool for navigating complex decision-making landscapes, applicable in a wide array of fields, from shaping public policy to crafting nuanced business strategies [99][100][101][102][103][104][105][106][107][108][109].…”

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“…It meticulously organizes decisionmaking factors into a hierarchical structure of main and subfactors, determining their significance through pairwise comparisons. Supported by ratio scales, this hierarchical evaluation process enables decision-makers to ascertain weights and prioritize factors effectively, streamlining the decision-making process in intricate scenarios [55][56][57].…”