Improving efficiency and safety in aviation industry using big data analytics

In aviation industry a large flow of data including thousands of parameters are registered by FDRs (Flight Data Recorders). The objective of this project is to use this big data to improve the efficiency and safety of flights. The data is collected and segmented from the raw datasets and then proper data cleaning methods are used to preprocess data. Then, by the help of analytical models we define a baseline for different registered parameters and compare individual flights against the baseline to detect anomalies. In the next step, by the help of optimization models and statistical techniques such as clique partitioning and set partitioning methods, we define a normal flight and compare individual flights with this pattern.

Faculty Supervisor:

Gilles Savard

Student:

Shadi Sharif Azadeh

Partner:

CAE Inc.

Discipline:

Mathematics

Sector:

Aerospace and defense

University:

Polytechnique Montréal