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An integrated machine learning model for aircraft components rare failure prognostics with log-based dataset.
Maren David Dangut1, Zakwan Skaf2, Ian K Jennions1
1Integrated Vehicle Health Management (IVHM) Center, Cranfield University, Bedford, MK430Al, United Kingdom.
ISA Transactions
|May 20, 2020
Summary
Predictive maintenance in aerospace faces data imbalance challenges. A new hybrid approach using NLP and ensemble learning effectively predicts rare aircraft component failures, improving model performance.
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Area of Science:
- Aerospace Engineering
- Data Science
- Machine Learning
Background:
- Predictive maintenance is crucial for the aerospace industry, offering benefits like reduced downtime and enhanced service quality.
- Data-driven predictive modeling is hindered by data-imbalanced distributions, where certain classes significantly outnumber others, impacting temporal feature learning.
- Extreme class imbalance in aircraft maintenance logs presents a significant challenge for accurate failure prediction.
Purpose of the Study:
- To propose a novel hybrid machine learning approach for predicting extremely rare aircraft component failures.
- To address the data-imbalanced distribution problem in aircraft maintenance datasets.
- To improve the performance of predictive models in identifying infrequent but critical failure events.
Main Methods:
- A hybrid machine learning approach combining Natural Language Processing (NLP) techniques and ensemble learning was developed.
- The approach was validated using a real-world aircraft central maintenance system log-based dataset.
- Focus was placed on identifying patterns within the minority class to overcome class imbalance.
Main Results:
- The proposed hybrid approach demonstrated superior performance compared to existing imbalanced and ensemble learning methods.
- The model achieved higher precision, recall, and F1-score, outperforming the Synthetic Minority Oversampling Technique (SMOTE) by approximately 10%.
- Exclusive pattern searching within the minority class proved effective in mitigating the class imbalance issue.
Conclusions:
- The developed hybrid machine learning model successfully predicts extremely rare aircraft component failures.
- The NLP and ensemble learning combination effectively addresses data imbalance challenges in aerospace predictive maintenance.
- This approach significantly enhances model classification performance for critical, infrequent events.