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Anonymising and desensitising health and safety data.

30/04/2020

A major step to unlocking value and reducing accidents by automatically desensitising and anonymising over 1.5 million health and safety documents.

Startup deploys wireless sensors across ship’s cargo hold to predict fire.

03/03/2020

This pilot project trialled wireless sensors to detect thermal temperature increases on the below deck of a cargo ship.

Pre-fire Temperature Change Detection Onboard Ships.

12/01/2020

This challenge sought innovative solutions which could deliver real-time, early detection of such thermal level increases of transported vehicles.

Early detection of gas leaks onboard a Liquid Natural Gas (LNG) tankers

12/01/2020

This challenge set in conjunction with Shell, one of the largest oil and gas companies in the world, sought innovative methods to detect these minor gas leaks onboard an LNG tanker, using sensor technologies or other approaches.

Data analytics to detect pipeline faults.

12/01/2020

This Challenge, set in conjunction with Phillips 66, the US energy manufacturing and logistics company, sought innovative IoT, machine learning and AI solutions to more effectively identify anomalies from the immense ILI data sets.

Determining master’s and crew’s psychological and emotional health.

12/01/2020

The challenge set in partnership with Pacific International Lines (PIL), sought innovative methods to assess the psychological and emotional wellbeing of individual crew members in real-time, whilst they are onboard and before they are about to go on duty.

Automatic anonymising and desensitising of health and safety data

12/01/2020

This challenge, set in conjunction with the Discovering Safety Programme,  sought innovative, effective, automated techniques for desensitising and anonymising health and safety information which would ultimately enable a much more diverse set of health and safety information.

Startup improves safety using deep learning-based computer vision.

13/11/2019

This pilot trialled NumberBoost’s computer vision inspection technology powered by deep learning, to build a real-time error detection and feedback solution, to automatically detect anomalies in photos of work done on critical infrastructure.

Discover the safetytech solution that can assess if a crew is fit for duty.

13/11/2019

The pilot trialled Senseye’s Operational Risk Management solution on top of its existing Emotional Intelligence Engine a and computer vision-based pattern recognition to assess in real-time whether crew members were ‘fit for duty’ or not.

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