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Title
An interpretable operational state classification framework for elevators through Convolutional Neural Networks
Author
Olaizola Alberdi, Jon
Izagirre, Unai
Serradilla Casado, Oscar
Zugasti, Ekhi
Mendicute, Mikel
Aizpurua Unanue, José Ignacio
Research Group
Análisis de datos y ciberseguridad
Teoría de la señal y comunicaciones
Other institutions
Laboral Kutxa
Universidad del País Vasco/Euskal Herriko Unibertsitatea (UPV/EHU)
Ikerbasque
Version
Postprint
Rights
© The Authors
Access
Embargoed access
URI
https://hdl.handle.net/20.500.11984/6973
Publisher’s version
https://doi.org/10.1111/mice.13479
Published at
Computer-Aided Civil and Infrastructure Engineering  Early View
Publisher
Wiley
Abstract
Ensuring the safe, reliable, and cost-efficient operation of transportation systems such as elevators is critical for the maintenance of civil infrastructures. The ability to monitor the health state ... [+]
Ensuring the safe, reliable, and cost-efficient operation of transportation systems such as elevators is critical for the maintenance of civil infrastructures. The ability to monitor the health state and classify different operational states (elevator moving up/down, stopped, doors opening/closing) may lead to the development of intelligent solutions, such as diagnostics and predictive maintenance. Accordingly, downtime and maintenance costs can be significantly reduced with an accurate monitoring of the operation parameters and dynamics. In this context, this paper presents a novel approach for the operational state classification of elevator systems based on a one-dimensional convolutional neural network, using exclusively a single axis (Z) of an accelerometer signal. The proposed model utilizes a single accelerometer and addresses the challenge of distinguishing overlapping signal patterns, such as those produced by vertical displacement and door movements. The approach includes an interpretability stage, which demonstrates the data processing involved in extracting features from the underlying physical phenomena captured in the acceleration signal. Obtained results have been validated with an on-site captured dataset which contains 250 elevator journeys and compared with three other classification methods that have been conventionally used: generalized likelihood ratio test (GLRT), barometer-assisted GLRT, and three conventional machine learning modelss. It has been shown that the proposed approach is very accurate, with 96% of the average F1 score and, importantly, includes the analytic relation of the classification model features. [-]
Funder
Gobierno Vasco
Gobierno de España
Program
Ikertalde Convocatoria 2022-2023
Ramon y Cajal. Convocatoria 2022
Number
IT1451-22
RYC2022-037300-I
Award URI
Sin información
Sin información
Project
Teoría de la Señal y Comunicaciones (IKERTALDE 2022-2023)
Jose Ignacio Aizpurua Unanue
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  • Articles - Engineering [742]

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