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Tonic and University of Southampton Impactech collaborate on applied research

March 7th, 2017

Tonic Analytics and Impactech, the University of Southampton’s applied research interface with industry, are collaborating on a project to understand the effects of air quality on aircraft system reliability.

Aircraft technical faults result in huge impact across the global economy that can be measured in Billions of dollars a year.   Delays are costly for airlines and their passengers. A 2010 study commissioned by the Federal Aviation Administration estimated that flight delays cost the airline industry $8 billion a year, much of it due to increased spending on crews, fuel and maintenance. Delays cost passengers even more — nearly $17 billion. Around 1/3 of all delays are attributable to the airline itself (rather than weather, security or air traffic for example).  Of these, the majority are due to aircraft technical issues.

While aircraft spend much of their time cruising at altitude in relatively clean air, they also experience take off, landing and turn-around at airports, where air quality can be poor.  There are many systems in a modern aircraft that utilise air in their operation – such as air conditioning; ventilation; engine starting and indeed the engines themselves.  Operating in regions with poor air quality can therefore expose components in these systems to an operating environment that can have an adverse effect on their reliability.

The project aims to identify appropriate sources of data that can characterise environmental conditions at locations across the globe, explore methods to blend this data with existing big data and demonstrate appropriate methods of data visualisation.

By collaborating on this project with the University through Impactech, Tonic is aiming to ultimately deploy additional capabilities to its existing analytics intensive cloud based predictive maintenance services.  This will further reduce disruption to airline operations while also enabling the supply chain to better understand the life expectancy of on-wing components in its support models.

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