Predictive aircraft maintenance: Difference between revisions
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Client: Adam Durant, [[Satavia]] - <adam.durant@satavia.com> | Client: Adam Durant, [[Satavia]] - <adam.durant@satavia.com> | ||
Local company Satavia helps airlines and aircraft engine manufacturers to schedule maintenance based on the amount of exposure the components have had to air pollution, dust, ice, volcanic ash and other environmental factors. They have large data sets which could be used to train predictive models that might be added to the Microsoft Cortana Intelligence Solution Template Playbook (assistance from Microsoft will be available) for predictive maintenance in aerospace. You will need to deliver a data ingestion architecture for a range of global data, and also demonstrate an aircraft maintenance scheduling application based on machine learning that applies the results. | Local company Satavia helps airlines and aircraft engine manufacturers to schedule maintenance based on the amount of exposure the components have had to air pollution, dust, ice, volcanic ash and other environmental factors. They have large data sets which could be used to train predictive models that might be added to the Microsoft Cortana Intelligence Solution Template Playbook (assistance from Microsoft Research will be available) for predictive maintenance in aerospace. You will need to deliver a data ingestion architecture for a range of global data, and also demonstrate an aircraft maintenance scheduling application based on machine learning that applies the results. | ||
Latest revision as of 15:32, 9 November 2017
Client: Adam Durant, Satavia - <adam.durant@satavia.com>
Local company Satavia helps airlines and aircraft engine manufacturers to schedule maintenance based on the amount of exposure the components have had to air pollution, dust, ice, volcanic ash and other environmental factors. They have large data sets which could be used to train predictive models that might be added to the Microsoft Cortana Intelligence Solution Template Playbook (assistance from Microsoft Research will be available) for predictive maintenance in aerospace. You will need to deliver a data ingestion architecture for a range of global data, and also demonstrate an aircraft maintenance scheduling application based on machine learning that applies the results.
