And then there was AI: Deutsche Bahn’s experience (RailEngineer)

Rail Engineer Issue 136 described many operators using Remote Condition Monitoring (RCM) and a few pioneers making baby steps in Condition-Based Maintenance (CBM). Roll forward to 2023, and there was a consensus that RCM has come of age and CBM has, perhaps, progressed to toddler status. Many presenters, referring to data lakes, implied being overwhelmed by with data and struggling to gain useful insights. Some were taking baby steps with machine learning and Artificial Intelligence (AI). This article covers the highlights.

Railways are still struggling to recover their pre-Covid revenue and often cannot reduce services whilst under pressure to cut cost. This was emphasised by Ian Rawlings, TfL’s Head of Vehicles Engineering. who explained that, in London, there’s now far less travel for work purposes whilst leisure travel is exceeding pre-Covid levels especially at weekends. He said that the Elizabeth line is performing ahead of expectations and has drawn traffic from, especially, the Central and Jubilee lines. Ian cited different thinking represented by the recent trial of a midweek engineering closure on the UK East Coast Main Line, leading him to wonder whether TfL could, perhaps, close the central area of the Central line for upgrade to allow track, signalling, and rolling stock to be more efficiently upgraded.

Passengers could use the Elizabeth line instead. Would a five-month closure be better than five years of weekend closures, he mused? Ian also described the range of techniques his team have employed to double, gradually, the interval between overhauls on the 192-train S stock fleet from 762,000 km to 1,524,000 km using condition assessments at the higher mileage, failure data, and materials usage.

Data, data, everywhere

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