Bogie Stability Control and Management Using Data Driven Analysis Techniques for High-Speed Trains

Abstract

The bogie is a critical component of a train set. Any failure in the bogies’ structure/mechanism transmission will result in a derailment in the revenue operation. In order to prevent derailment due to bogie failures, the Bogie Instability Detection System (BIDS) is installed on high-speed trains, which provides alarms when abnormality is detected so that appropriate action can be taken. Taking the Taiwan High Speed Rail Corporation (THSRC) as an example, there have been ongoing reports of BIDS alarms since its launch. In almost every case, intermittent warnings are generated when wheel/rail interface faults occur, but no hard fault is found in the bogie’s structure or mechanism. While these reports do not constitute hard faults, corresponding operation actions and inspections are necessary nonetheless. As a result, delays and additional maintenance costs are incurred. In order to save maintenance costs and reduce delays, a decision tree algorithmic approach is proposed to study the potential factors of wheel/rail interface concerns, discover relationships between potential factors, and set up rules for BIDS alarms. In this case study, we found that temperature is the main factor for BIDS alarms; train speed, accumulated mileage, and location are subordinate factors. The proposed decision tree approach can lead to some improvements in preventive maintenance. It also suggests embedding such an intelligent algorithm into BIDS control units for drivers to avoid BIDS alarms.