ML-Based Aircraft Engine Preventive Maintenance
Budget: €250 – €750 EUR
Hello ,
I need your help to implement this topic.
"Preventive Maintenance for Aircraft Engines: Analysis of ML Models to find optimal parameter control ranges”
Problem Statement: Find control ranges for parameters to prevent engine failure scenarios through the implementation of ML models.
Objective:
• The objective of this study is to develop an efficient and reliable ML model for PdM of aircraft engines.
• The primary focus is doing a comprehensive study of existing PdM techniques and ML models used in the aviation industry as base models.
• This study also aims to identify key indicators of engine health and optimise the control parameters to remain within tested ranges obtained through the model and methodology.
• The impact of this study would be significant, potentially reducing maintenance costs, minimizing downtime, and improving overall operational efficiency in the aviation industry.
1. Oversampling, and Undersampling before modelling to make sure to have balanced data.
2. Focus on each area of failure and non-failure state after modelling and see the accuracy
3. Use the best-chosen model which predicts the Failure state properly, to be fed into PDP and SHAP to see the ranges of control parameter framework for user alerting.
4. Genetic algorithm, Random forest, Support Vector Machine
Thanks in advance
I need your help to implement this topic.
"Preventive Maintenance for Aircraft Engines: Analysis of ML Models to find optimal parameter control ranges”
Problem Statement: Find control ranges for parameters to prevent engine failure scenarios through the implementation of ML models.
Objective:
• The objective of this study is to develop an efficient and reliable ML model for PdM of aircraft engines.
• The primary focus is doing a comprehensive study of existing PdM techniques and ML models used in the aviation industry as base models.
• This study also aims to identify key indicators of engine health and optimise the control parameters to remain within tested ranges obtained through the model and methodology.
• The impact of this study would be significant, potentially reducing maintenance costs, minimizing downtime, and improving overall operational efficiency in the aviation industry.
1. Oversampling, and Undersampling before modelling to make sure to have balanced data.
2. Focus on each area of failure and non-failure state after modelling and see the accuracy
3. Use the best-chosen model which predicts the Failure state properly, to be fed into PDP and SHAP to see the ranges of control parameter framework for user alerting.
4. Genetic algorithm, Random forest, Support Vector Machine
Thanks in advance