An Efficient Anomaly Detection for High-Speed Train Braking System Using Broad Learning System

For a high-speed train, the braking system plays an essential role in safe transportation. Efficient state monitoring and anomaly detection may provide useful information for real-time decisions. With large amount of monitoring data, data-driven methods, especially deep learning methods are widely adopted for anomaly detection in various industrial applications. Although deep learning methods have advantages in discovering non-linear relations among complex and high-dimensional data, the large amount of hyperparameters can be hardly well tuned with a high computational burden for onboard computers. Therefore, in this work an efficient online anomaly detection model based on Broad Learning System (BLS) is established for detecting anomalies in the braking system. Furthermore, considering the intrinsic imbalanced data size on anomaly and normal states, the cost-sensitive learning method is integrated in the BLS model, for the first time. The proposed model is evaluated on real data collected from a high-speed train operating for one year, with respect to two performance metrics, i.e. G-mean and F1-score. Comparisons with benchmark neural networks and the combinations of sampling methods and BLS are also considered in this work.