ElBowTie proves a formal means of allocating data to causes, consequences and risk mitigation measures set within an enterprise data analytical framework.
We are developing a tool that will flag up potential hazardous system states in real-time so that the appropriate action can be taken. The tool monitors numerous data feeds to provide a real time view of the resilience status of the system under study. Changes from the ‘normal resilient’ state to a state of ‘lower resilience’ are then identified and a warning signal generated.
ElBowTie is built around the well-known Bow Tie assessment methodology and has been designed to incorporate Big Data into the risk assessment activities.
The Bow-Tie analysis has been enhanced through linking relevant Enterprise data Taxonomy sources to each of the elements of the Bow Tie. These data sources include, for example, condition monitoring, social media, and safety management
The concept of Big data and associated analytics was not on the map ten years ago but now it looks like it will be revolutionary for the rail industry.
There is a real opportunity to integrate Big Data within railway safety risk assessment activities, giving timely and relevant information to support intelligent design decisions and ensure risk assessments are fully integrated into the business model for the rail.
To find out more watch our presentation “The Development of an Enhanced Elbow-Tie Railway Safety Assessment Tool using Big Data Analytics Approach” at the recent Event: IET International Conference on Railway Engineering (ICRE) be held from the 12th to the 13th of May 2016 in Brussels, Belgium.