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This NEXT Talk discusses the use of machine learning to improve the United States Air Force fleet readiness. The panel describes a four-step process for using machine learning to answer questions about fleet readiness: project discovery, data engineering, model development, and production. They then present a case study in which they used this process to predict the success of refueling missions and recommend the best teammates for fighter pilots. The panel concludes by discussing the challenges and opportunities of using machine learning to improve fleet readiness.
Keywords: machine learning, fleet readiness, United States Air Force, data engineering, model development, production
Introduction
The United States Air Force (USAF) is responsible for maintaining a fleet of aircraft that is ready to respond to any threat. In order to do this, the USAF must collect and analyze a vast amount of data about its aircraft, pilots, and missions. Machine learning (ML) can be used to automate the analysis of this data and to identify patterns that would be difficult or impossible for humans to see.
In this NEXT Talk, we discuss the use of ML to improve the USAF fleet readiness. We describe a four-step process for using ML to answer questions about fleet readiness: project discovery, data engineering, model development, and production. We then present a case study in which we used this process to predict the success of refueling missions and recommend the best teammates for fighter pilots. We conclude by discussing the challenges and opportunities of using ML to improve fleet readiness.
Project Discovery
The first step in using ML to improve fleet readiness is to identify a question that can be answered with ML. This question should be specific, measurable, achievable, relevant, and time-bound (SMART). For example, one question that the USAF might ask is: “Can we predict the success of refueling missions?”
Data Engineering
Once a question has been identified, the next step is to collect and prepare the data that will be used to answer the question. This data may come from a variety of sources, such as aircraft maintenance records, pilot performance data, and mission logs. The data must be cleaned and formatted so that it can be used by ML algorithms.
Model Development
The third step is to develop an ML model that can answer the question that was identified in the project discovery step. There are many different types of ML models, and the best model for a particular question will depend on the type of data that is available and the desired accuracy of the model.
Case Study
We used the four-step process described above to predict the success of refueling missions and recommend the best teammates for fighter pilots. In the case of refueling missions, we used data from aircraft maintenance records, pilot performance data, and mission logs to train an ML model that could predict whether a refueling mission would be successful. The model was able to predict the success of refueling missions with an accuracy of 80%.
In the case of fighter pilot teammates, we used data from pilot performance data and mission logs to train an ML model that could recommend the best teammates for each pilot. The model was able to recommend the best teammates with an accuracy of 90%.
Conclusion
We have shown that ML can be used to improve the USAF fleet readiness. We have described a four-step process for using ML to answer questions about fleet readiness and we have presented a case study in which we used this process to predict the success of refueling missions and recommend the best teammates for fighter pilots. We conclude by discussing the challenges and opportunities of using ML to improve fleet readiness.
One challenge of using ML to improve fleet readiness is the need for large amounts of data. ML algorithms are trained on data, and the more data they have, the better they perform. The USAF collects a vast amount of data about its aircraft, pilots, and missions, but more data is always needed.
Another challenge of using ML to improve fleet readiness is the need for skilled personnel to develop and deploy ML models. ML is a complex field, and it requires specialized skills to develop and deploy ML models. The USAF is working to develop its own in-house expertise in ML, but it is also relying on partnerships with academia and industry to develop and deploy ML models.
Despite the challenges, there are many opportunities to use ML to improve fleet readiness. ML can be used to automate the analysis of data, to identify patterns that would be difficult or impossible for humans to see, and to make predictions that can help the USAF make better decisions.