Unsupervised anomaly detection for earthquake detection on Korea high-speed trains using autoencoder-based deep learning models

Unsupervised anomaly detection for earthquake detection on Korea high-speed trains using autoencoder-based deep learning models

Blog Article

Abstract We propose a method for detecting earthquakes for high-speed trains based on unsupervised anomaly-detection techniques.In particular, Crafting Materials we utilized autoencoder-based deep learning models for unsupervised learning using only normal training vibration data.Datasets were generated from South Korean high-speed train data, and seismic data were measured using seismometers nationwide.The proposed method is compared with the conventional Short Time Average over Long Time Average (STA/LTA) model, considering earthquake detection capabilities, focusing on a Peak Ground Acceleration (PGA) threshold of 0.

07, a criterion for track derailment.The results show that the proposed model exhibit improved earthquake detection capabilities than STA/LTA for PGA of 0.07 or higher.Furthermore, the proposed model reduced false earthquake detections under normal operating conditions and accurately identified normal states.

In contrast, the STA/LTA method demonstrated a high rate of false earthquake detection under normal operating conditions, underscoring its propensity for inaccurate detection in many Croped t-shirt instances.The proposed approach shows promising performance even in situations with limited seismic data and offers a viable solution for earthquake detection in regions with relatively few seismic events.