An interpretable operational state classification framework for elevators through convolutional neural networks

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 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.