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The project aims to perform vibrational analysis on the Tail 653 NASA dataset, which contains sensor reading of different attributes from an aircraft's tail section during flight. The goal is to build a machine learning model for health monitoring of the aircraft based on the frequency analysis of the vibration data.

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Srivatsanray/Tail-653-Flight-Data-Analysis

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Tail-653-Flight-Data-Analysis

Description:

In this repository, we have tail 653 flight's data for analysis for the detection of anamolies in the aircraft engine to help with aircraft maintenance.

Our Tail 653 aircraft is represented by 8 flights, each a zip file. Each zip file contains about 650 individual matlab files.

The names of the matlab files use a naming convention. The file names are fixed length can be parsed by the number of characters. For example, if a file is named “652200101092009.mat”, then:

  • 652 = tail ID

  • 2001 = year

  • 01 = month

  • 09 = day

  • 20 = hour

  • 09 = min

The link to the data: https://c3.ndc.nasa.gov/dashlink/members/4/resources/?type=ds

Our reference research paper: https://ieeexplore.ieee.org/document/8361511

Key points from the research paper: https://docs.google.com/document/d/1FZSmRY9rOHgaNhxUc6Jp5NQM58naxhGpALTHrVUjGQw/edit?usp=sharing

Going ahead with preprocessing: https://docs.google.com/document/d/1kVnRk63tSMOp1wfyopW8_8w96p-sz2IP4fnh762tzBU/edit?usp=sharing

Important parameters to go ahead with: https://docs.google.com/document/d/1GRh7Yk-36YsXbfjd1AyD81fcHG2x8teIecTTfc8catc/edit?usp=share_link

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The project aims to perform vibrational analysis on the Tail 653 NASA dataset, which contains sensor reading of different attributes from an aircraft's tail section during flight. The goal is to build a machine learning model for health monitoring of the aircraft based on the frequency analysis of the vibration data.

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