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Track Risk Management Software (TRIMS)

TWI have integrated machine-learning tools with decision support tools (with reference to the second strand shown below).

The (rail) track risk management software (TRIMS) is an example.

  • The software develops risk profiles of track segments, where ‘risk’ is a combination of susceptibility to failure (SoF) and the consequence of failure (CoF)
  • SoF is determined using AI/machine learning techniques on databases that include maintenance data, geographical information, weather data and population density
  • CoF is currently determined using Network Rail’s MAC code but can be customised to end-user requirements
  • The risk profile of the track system comprising track segments is shown on a user-friendly risk matrix (see figure, below) that will support maintenance personnel in identifying high risk track segments, thereby enabling them to focus inspection and maintenance on critical track segments and carry out risk mitigation actions, such as installing sensors
  • Where time-based inspection regimes are in place, the approach can support better preparation and planning by providing advance estimates of risk

TRIMS builds on an Innovate UK funded project; more information can be found here

Avatar Ujjwal Bharadwaj Manager, Asset Life Cycle Management

Ujjwal leads a team of experts developing bespoke decision support approaches and software to enable asset operators make optimum decisions based on a variety of criteria, particularly those leading to measurements of risk and life cycle sustainability. The types of decisions that Ujjwal and his team provide support for include when and where to inspect and how to optimise run, replace, life-extend or decommission decisions; this requires establishing the condition of the asset system, at times requiring the use of data analytics, including machine learning.

Ujjwal has been with TWI since 2005 and is now manager of the Asset Life Cycle Management team in the Asset Integrity Management section at TWI. He is a risk management professional with more than 25 years of experience. He is a Chartered Engineer (CEng), with a Doctor of Engineering (EngD) degree from Loughborough University based on his work on approaches to risk-based life management of offshore structures and equipment. He read for an MSc in Risk Management from the London School of Economics (LSE) and a Bachelor of Engineering (BE) degree in Electrical and Electronics engineering from Mangalore University, India.