Description
Practical use of wheel impact load detector data for detecting vehicle defects in freight wagonsFreight wagon suspension systems and components can deteriorate and degrade over time, leading to altered dynamic characteristics that may adversely affect a vehicle’s resistance to derailment. In Great Britain (GB) rail, vehicle defects have been identified as causal or contributory factors in several freight train derailments, prompting the Rail Accident Investigation Branch (RAIB) to recommend that the industry take action to address the causes, effects, and monitoring of defect-related uneven wheel loads. To support this, there is a clear need for a practicable and efficient method of regularly monitoring wagons for defects, enabling timely detection and intervention before safety is compromised.
This research builds on earlier work that demonstrated the potential of using Wheel Impact Load Detector (WILD) data to identify defects in freight vehicles. In the current study, analytics have been routinely applied to a continuous data stream covering over 6,000 freight wagons. The experience gained highlights the value of this approach for early detection of defects that may compromise derailment resistance. The research also explores the practical use of this information in fleet management, including methods for localising and categorising defects, assessing their severity, and developing a framework for setting appropriate intervention thresholds.
| Period | 18 Nov 2025 |
|---|---|
| Event type | Conference |
| Conference number | 14th |
| Location | Colorado Springs, United States, ColoradoShow on map |
| Degree of Recognition | International |