Publications
Academia
- Machine Learning based Diagnostic Approach to Condition Monitoring of Railway Platform Screen Door Systems, April 2023
- Modeling and Simulation of Platform Screen Door (PSD) System using MATLAB-SIMULINK
- A New Fault Detection and Classification Approach for Platform Screen Door Systems Using Artificial Neural Networks
ECOMAI Project
- Penta ECOMAI Project Profile from EURIPIDES² / AENEAS, 2022
Commercial
Academia
Machine Learning based Diagnostic Approach to Condition Monitoring of Railway Platform Screen Door Systems
Authors
Şükrü Görgülü*1, İsa Koç 2, Necim Kırımça 3, Ömer Mermer 4, Mehmet Karaköse 5, Mehmet Tankut Özgen 6
1,6 Dept. of Electrical and Electronics Engineering, Eskişehir Technical University, Türkiye
2 Dept. of Electrical and Electronics Engineering, Eskişehir Osmangazi University, Türkiye
5 Dept. of Computer Engineering, Elazığ Fırat University, Türkiye
1,2,3,4 Albayrak Makine Elektronik San. Tic. A.Ş. Eskişehir, Türkiye
Abstract
Paper presented at 2nd International Conference on Engineering, Natural and Social Sciences on April 4-6, 2023 : Konya, Turkey
This study focuses on the use of machine learning-based fault classification to investigate the condition monitoring of Platform Screen Door (PSD) Systems in the railway industry. PSDs are safety systems installed in subway or train stations to prevent passengers from falling onto the tracks. The electromechanical system consists of sliding doors or gates that run along the platform edge and only open when the train is properly aligned with the platform.
Modeling and Simulation of Platform Screen Door (PSD) System using MATLAB-SIMULINK
Authors
R&D Department Albayrak Makine Elektronik A.Ş.
Eskişehir Vocational School, Eskisehir Osmangazi University
Department of Computer Engineering, Fırat University
Abstract
In this paper, electromechanical modeling of an industrial platform screen door (PSD) system has been developed. The system incorporates several sub-units such as PMSM, motor controller, inverter, and mechanical unit.
Moreover, a newly designed motor control system based on real operation conditions is introduced. In addition, modeling procedures are described, and simulation results are presented.
Furthermore, numerical simulation in accordance with specific velocity and position references are provided for verifying the effectiveness of the proposed modeling. Furthermore, the results show that the developed electromechanical model of PSD system is effective in managing the parameters that affect the actual operating state of the PSD sliding door system. These modelling and simulation will play an important role.
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
ECOMAI [ Ecological Motor Control and Predictive
Maintenance with AI]
Project Profile
by EURIPIDES² and PENTA