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Preventive maintainence of Aircraft engines

Project done in collaboration with Rolls-Royce and Imperial College London as part of Final year capstone project

Technologies used: Python, Machine learning Timeline : Jan 2022 - Dec 2022

1. Presented Scarecrow - Intelligent Annotation platform for Engine Health Management in AI ML Systems conference
2. Predictive maintainence of aircraft engines in 9th International Conference on Business Analytics and Intelligence (Best paper award)


Team: Solo project(academic) and 3 member team (implementation)

Problem Statement: Predict failure of specific parts in an aircraft engine

  1. Academic project: Explored various unsupervised failure identification methods on aircraft engine simulated data. Explore various ways of implementing said methods to predict failure in engines
  2. Implementation in industry: Implemented a novel autoencoder-decoder model to predict the ideal behavior of 250 plus parameters in an aircraft engine. This helped engineers identify anomalous behaviors of aircraft engines on test beds

Solution 1. Different failure modes and degradation scenarios were observed, and three different unsupervised approaches were suggested
2. Simulated data from CMAPSS was taken to test the different methods on real failure modes on aircraft engine data
3. Implemented a novel autoencoder-decoder-based approach to predict the ideal behaviour of more than 250 parameters in steady and transient phases of flight
4. Detected anomalies on test bed experiments using z-scores and CUSUM

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