Due to the scarcity of abnormal condition data in components of transportation systems, only normal condition data are typically used to train models for anomaly detection. One of the main challenges is the difficulty of properly representing the data distribution which is typically non-smooth, high-dimensional and on a manifold. This work develops an anomaly detection model based on an Auto-Encoder (AE) formed by the generator of a Generative Adversarial Network (GAN) and an auxiliary encoder to capture the sophisticated data structure. The reconstruction error of the AE is, then, used as anomaly score to detect anomalies. Additionally, an adaptive noise is added to the data to make easier the GAN optimization, an AdaBoost-based ensemble learning scheme is used to improve detection performance and a new approach for setting the hyperparameters of the AE-GAN model based on the derivation of a lower bound of the Jensen-Shannon divergence between generator and normal condition data distributions is developed. The method has been applied to synthetic and real data collected from automatic doors of high-speed trains.

Generative Adversarial Networks With AdaBoost Ensemble Learning for Anomaly Detection in High-Speed Train Automatic Doors

Mingjing Xu;Piero Baraldi;Xuefei Lu;Enrico Zio
2022-01-01

Abstract

Due to the scarcity of abnormal condition data in components of transportation systems, only normal condition data are typically used to train models for anomaly detection. One of the main challenges is the difficulty of properly representing the data distribution which is typically non-smooth, high-dimensional and on a manifold. This work develops an anomaly detection model based on an Auto-Encoder (AE) formed by the generator of a Generative Adversarial Network (GAN) and an auxiliary encoder to capture the sophisticated data structure. The reconstruction error of the AE is, then, used as anomaly score to detect anomalies. Additionally, an adaptive noise is added to the data to make easier the GAN optimization, an AdaBoost-based ensemble learning scheme is used to improve detection performance and a new approach for setting the hyperparameters of the AE-GAN model based on the derivation of a lower bound of the Jensen-Shannon divergence between generator and normal condition data distributions is developed. The method has been applied to synthetic and real data collected from automatic doors of high-speed trains.
2022
AdaBoost ensemble learning; anomaly detection; generative adversarial networks; high dimensional time series; High-speed train automatic door; manifold distribution
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1227347
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