Improving the adoption of data analytics for the detection of rail vehicle defects using wheel impact load detector data

The suspension systems of railway freight wagons can deteriorate and degrade over time, altering the suspension characteristics and potentially adversely influencing derailment resistance. On the most part such degradation is adequately managed through routine inspection and maintenance activities. However, reduced wheel load as a consequence of suspension or vehicle defects has been identified as a causal or contributory factor in a number of freight train derailments in Great Britain. Previous research has demonstrated the feasibility of using existing data feeds from trackside instrumentation to reveal abnormal wagon ‘footprints’ which can indicate wagon defects and elevated derailment risk. Despite the promising findings of this research, a number of barriers to implementation have prevented widespread adoption of the low-cost analytics. This proof-of-concept project will address a significant number of those implementation barriers, bringing actionable information closer to the end-user, supporting wider adoption and accelerating impact from the underlying research.