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DOI: 10.14569/IJACSA.2020.0111105
PDF

Autoencoder based Semi-Supervised Anomaly Detection in Turbofan Engines

Author 1: Ali Al Bataineh
Author 2: Aakif Mairaj
Author 3: Devinder Kaur

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 11 Issue 11, 2020.

  • Abstract and Keywords
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Abstract: This paper proposes a semi-supervised autoencoder based approach for the detection of anomalies in turbofan engines. Data used in this research is generated through simulation of turbofan engines created using a tool known as Commercial Modular Aero-Propulsion System Simulation (CMAPSS). C-MAPSS allows users to simulate various operational settings, environmental conditions, and control settings by varying various input parameters. Optimal architecture of autoencoder is discovered using Bayesian hyperparameter tuning approach. Autoencoder model with optimal architecture is trained on data representing normal behavior of turbofan engines included in training set. Performance of trained model is then tested on data of engines included in test set. To study the effect of redundant features removal on performance, two approaches are implemented and tested: with and without redundant features removal. Performance of proposed models is evaluated using various performance evaluation metrics like F1-score, Precision and Recall. Results have shown that best performance is achieved when autoencoder model is used without redundant feature removal.

Keywords: Anomaly detection; autoencoder; bayesian hyperparameter tuning; turbofan engine

Ali Al Bataineh, Aakif Mairaj and Devinder Kaur, “Autoencoder based Semi-Supervised Anomaly Detection in Turbofan Engines” International Journal of Advanced Computer Science and Applications(IJACSA), 11(11), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0111105

@article{Bataineh2020,
title = {Autoencoder based Semi-Supervised Anomaly Detection in Turbofan Engines},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0111105},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0111105},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {11},
author = {Ali Al Bataineh and Aakif Mairaj and Devinder Kaur}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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