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A New Fault Detection and Classification Approach for Platform Screen Door Systems Using Artificial Neural Networks

Authors

R&D Department Albayrak Makine Elektronik A.Ş.
Department of E&E Engineering Eskişehir Osmangazi University
Department of Computer Engineering, Fırat University

Abstract

Platform Screen Door (PSD) system, which is called a safety-critical system, is a sliding barrier door installed on the sides of a platform in many modern metros and RBT (Rapid Bus Transit) stations. Failures that may occur in the PSD system will seriously affect the availability of train transportation as well as the passenger’s safety. In this study, data-driven fault detection and classification method have been studied on the PSD system to ensure the safe and reliable operation. An artificial neural network (ANN) is preferred because of its powerful capabilities. Different operating conditions (normal and faulty) were created artificially on the PSD and the motor current, motor voltage, and door speed signals were used as input dataset. Datasets were collected over 1000 on/off cycles and related 18 features were calculated for each operating condition. Different parameters (features, neuron numbers, and input signals) were investigated and performance metrics such as accuracy, sensitivity, and precision were calculated comparatively. According to the results, ANN with three layers (input-hidden-output) and the number of neurons 12-9-7, respectively, show the best performance. In addition, the highest accuracy value (%97.1) is obtained when the motor current and motor voltage are taken together as the input signal. Consequently, it is observed that the ANN structure is a useful AI tool in fault detection on the PSD system.

ECOMAI Project