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June 15, 2026Journal of the Korean society for railway

Autoencoder Model Design Method for Operational Anomaly Detection in Escalators

In-Keun Lee Jun-Seok Park Chi-Won Kang

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Key Points

This research aims to develop an autoencoder model design method specifically for detecting operational anomalies in escalators.
Utilized current data collected from escalators for grid search to select optimal hyperparameter combinations.
Performed unsupervised learning based on the optimal hyperparameters to reflect the specific characteristics of various escalators.
Evaluated the model using real operational escalator data.
The proposed design method effectively builds customized anomaly detection models for each escalator.
Demonstrated improvements in operational efficiency and maintenance strategies through the application of the model.

Implication

Randomized design proposes a method for anomaly detection in escalators, suggesting enhanced equipment management.

Abstract

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Cite This Study

Lee et al. (2026) studied this question.

synapsesocial.com/papers/6a3239c2d50b63ecad2051f2 https://doi.org/10.7782/jksr.2026.29.6.568

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