# Wavelet-Based Analysis of Motor Current Signals for Detecting Obstacles in Train Doors
## 1 Introduction
### Related Work
In the present research, wavelet transform is used to detect abrupt changes in a current signal, extract obstacle detection features, and identify soft and hard objects. We also developed a wavelet-transform-based simulation model for detecting changes in a motor current signal. Peaks in the motor current caused by a door obstruction are detected using the Daubechies (db10) mother wavelet to generate wavelet coefficients. By detecting peaks in these coefficients, obstacle features are extracted and rules for detecting soft and hard objects are determined. To validate the obstacle detection rules, an experimental device was constructed and controlled by a developed program. Moreover, the system was verified to comply with the European standard EN 14752:2019+A1:2021 [23], which specifies the required sensitivity and peak force for \begin{table}
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**Research (Authors and Year)** & **Detection Variable** & **Detection Method** & **Major Contributions** & **Datasets Used** & **Domain-Specific Aspects** \\ \hline [PERSON] et al., 2024 [2] & Foreign objects present between PSD and train doors & Differentiation neural net & Foreign object detection with segmentation & Synthetic and real-world data & Enhanced precision in complex scenarios \\ \hline [PERSON] et al., 2023 [3] & Anomalous objects or events between PSD and train doors & Key frames extraction & Anomaly detection & Intercity rail station anomaly (IRSA) dataset & High precision and outperforming the state-of-the-art \\ \hline [PERSON] et al., 2022 [4] & Presence of objects between PSD and train doors & Deep learning (CNN) & Object detection between train and platform & Custom video dataset & High accuracy, tested on real-world data \\ \hline [PERSON], 2020 [5] & Motor voltage & Back EMF measurement & Reduced design cost, proposed cost-effective solution & Motor voltage signal & Not effective and not accurate \\ \hline [PERSON]. [PERSON] et al., 2019 [6] & Motor voltage & Ripple counting method & Reduced design cost, cost-effective solution & Motor voltage signal & Effective but not accurate \\ \hline [PERSON], [PERSON] et al., 2019 [7] & Motor current & Current tripple width, and position of the anti-pinch area & Cost effective, improves the safety and reliability, in accordance with GR7258-2017 & Motor current signal & Effective but not accurate \\ \hline [PERSON], [PERSON] et al., 2021 [8] & Motor current & Wavelet transform & Efficient, useful in motor fault analysis, time frequency localization & Motor current signal & Effective and accurate \\ \hline [PERSON] et al., 2012 [10] & Motor current & Fast Fourier transform & Early-stage fault detection, non-invasive technique & Motor current signal & Effective but not accurate \\ \hline Sensitive Edge Sensors Study & & Sensor-based detection & Obstacle detection using physical contact & Real-world data & Compliance with EN14752 \\ \hline \hline \end{tabular}
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Table 1: Literature analysis of stall-detection methods for DC motors. obstacle detection methods applied in body-side entrance systems for rolling stock. The scope of this standard applies for obstacle detection and testing. It specifies the sensitivity and peak force for testing the obstacle detection [23]. The main contributions of the proposed method are as follows:  
1. Development of a cost-effective, sensorless obstacle detection system for train doors using wavelet-based current signal analysis. 2. Validation of the proposed method's ability to differentiate between soft and hard objects and detect obstacles within 0.3 s, meeting the requirements of the EN14752 standard. 3. Demonstration of the system's practicality through simulation and testing on a downscaled test bench. 4. Discussion of the potential for future optimization, including microchip implementation for cost-efficient real-world deployment.