Parallel resonant permanent-magnet synchronous generator (PMSG) systems, which consist of a diesel engine, PMSG with a resonant parallel capacitor, and diode full-wave rectifier, may potentially be applied to series hybrid vehicle traction systems owing to their high efficiency and low cost. In general, the power generation in series hybrid vehicle traction systems is controlled using a pulse-width modulated (PWM) converter. However, the power generation in a parallel resonant PMSG hybrid traction system cannot be adjusted using a PWM converter and a new power-generation control method is required. An appropriate power generation control method that considers battery deterioration, number of engine-starts, and fuel economy has not been developed yet. Therefore, this study proposes a power generation control method for parallel resonant PMSG systems applied to series hybrid vehicle traction systems.
In recent years, some railway vehicles have been equipped with condition monitoring devices, which constantly record the operating condition of railway vehicle equipment. For more effective use of condition monitoring devices, we propose an anomaly detection method for railway vehicle equipment using Long Short-Term Memory (LSTM), which is a deep learning method suitable for learning time-series data. In this paper, we apply the proposed method to data on engines and air-conditioning units recorded on vehicles in operations. Results confirmed that the anomaly score for anomalous data increases by using the proposed method, and that anomalies are detected in railway vehicle equipment before faults appear.
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