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Projects: Projects for Investigator
Reference Number ENA_10068173
Title Predictive Safety Interventions - Beta
Status Started
Energy Categories Fossil Fuels: Oil Gas and Coal(Oil and Gas, Refining, transport and storage of oil and gas) 100%;
Research Types Applied Research and Development 100%
Science and Technology Fields SOCIAL SCIENCES (Business and Management Studies) 20%;
ENGINEERING AND TECHNOLOGY (Mechanical, Aeronautical and Manufacturing Engineering) 80%;
UKERC Cross Cutting Characterisation Not Cross-cutting 100%
Principal Investigator Project Contact
No email address given
Award Type Network Innovation Allowance
Funding Source Ofgem
Start Date 01 August 2023
End Date 01 February 2025
Duration 18 months
Total Grant Value £1,189,696
Industrial Sectors Energy
Region South East
Investigators Principal Investigator Project Contact , SGN (99.999%)
  Other Investigator Project Contact , SGN - Scotland (0.001%)
  Industrial Collaborator Project Contact , Northern Gas Networks (0.000%)
Project Contact , Cadent Gas (0.000%)
Web Site https://smarter.energynetworks.org/projects/10068173
Abstract "The PSI Beta project is fully aligned with the SIF Beta Challenge for Data and Digitalisation, and successful project completion will deliver the next generation of user driven digital products, services, and processes. The project will create a predictive safety model in the gas sector and ready to take to the wider energy sector and utility sector globally, aligned with the Beta challenge phase.According to HSE annually released statistics, at least 10,000 working days were lost to injury in the wider utility sector in the 21/22 financial year, with the estimated cost of fatal and non-fatal injuries more than 160m. The PSI has a clear and direct target to prevent the occurrences of fatal and non-fatal injuries, which will reduce the cost of operating energy networks, a direct objective and aim of the SIF challenge for Data and Digitalisation.FYLD has become the tool of choice to manage safety and productivity in the workforce at SGN and has delivered a 20% reduction in incidents and injuries alongside annual financial savings of c.4.5m. We forecast the safety improvement opportunity from successful PSI completion to be well beyond these outcomes, using predictive analytics to identify which workers and activities will have a safety incident and push teams to intervene and respond before they occur. FYLDs vision is to assist every fieldworker to take corrective actions and put unsafe conditions right in real time.n the Alpha phase, FYLD and SGN proved it was possible to accurately quantify risk scores in real time and prompt a preventative or mitigating action, deploying the machine-learning model in a proof of concept. The model drew on 3 different live-data inputs, delivering an accuracy of 57%. We demonstrated that the model accuracy was improved through an increased number of data sources, noting that applying the live weather to the model increased accuracy alone by 4%.Our problem understanding also grew in the Alpha phase with respect to the method of surfacing interventions. In our Alpha submission we targeted building and deploying a control suggestion, however during our governance sessions, we remained agile and built the capability to push high risk notifications - enabling an AI powered human intervention. The outcome was positive - we saw a 35%increase in the response rate from field teams in high-risk vs non-high-risk jobs. We hypothesise that we can increase this improved response through iterations, human validation of recommendations, and improved AI powered interventions, which will be delivered in the Beta phase.We can say with confidence that we are beginning to accurately predict the presence of a safety risk on site and intervene in real time.We will take this further in the Beta project, iterating the model through greater data sources. We will build the ability to capture and integrate live situational data from local traffic and roadworks, alongside human related factors such as fatigue, voice tone or behaviour changes. We will develop the object recognition to go beyond detecting objects on controls, and research the ability to detect where site set ups are non-compliant and contribute to safety risk.User Needs & PersonasFieldworkers face a reduced capacity to perceive risk on site due to overexposure, and differing capabilities mean individual risk tolerance varies by individual. The project will seek to address the lack of access to data of historic incidents or safety events, or the inability to drawa link between those safety events and risk factors that may be presentField team managers - expectations exist to interact in many risk assessments, but time demands mean that this cannot always be immediate. By enabling managers to focus their attention and prioritise sites identified as high risk from live data inputs, we can target a second set of eyes where it is needed most and shift away from ineffective sampling techniquesSenior and safety managers - further removed from site activities, senior and safety managers need to have confidence in, and the ability to visualise, risk management. Creating risk quantification and visibility via interactive dashboards, and the ability to performance manage the associated mitigation, or be alerted when risk hits unacceptable levels, are key drivers for this persona groupFYLD are best placed to assist SGN and bring this solution to market through:A high-performing team with experience launching and maintaining AI/ML products in the remote field service industryA demonstrable history of realising significant cost savings for utilities companies by deploying innovative solutionsExisting technology and datasets that can be built uponIn-house experience and expertise in change-management required for digital transformation, specifically within safety and productivity of utility companies, at scale"
Publications (none)
Final Report (none)
Added to Database 12/10/23