An intelligent risk management framework for monitoring vehicular engine health
Authors
Rahim, Md. AbdurRahman, Md Arafatur

Rahman, Md Mustafizur
Zaman, Nafees
Moustafa, Nour
Razzak, Imran
Issue Date
2022-05-31
Metadata
Show full item recordAbstract
The unwanted vehicular engine irregularities diminish vehicular competence, hinder productivity, waste time, and sluggish personal/national economic growth. Transportation sectors are essential infrastructures that require practical vulnerability assessment to avoid unexpected consequences through risk severity assessment. Artificial intelligence would be vital in the Industry 4.0 era to eliminate these issues for seamless activity and ultimate productivity. This article presents a risk management framework that includes an efficient decision model for monitoring and diagnosing vehicular engine health and condition in real-time using vulnerable components information and advanced techniques. To do this, we used the vulnerability identification frame to identify the vulnerable objects. We created a decision model that used an infrastructure vulnerability assessment model and sensor-actuator data to diagnose and categorise engine conditions as good, minor, moderate, or critical. We used machine learning and deep learning algorithms to assess the effectiveness of the risk management system’s decision model. The stacked ensemble of the deep learning algorithm outperformed other standard machine learning and deep learning algorithms in providing 80.3% decision accuracy for the 80% training data and efficiently managing large amounts of data. Anticipating the proposed framework might assist the automotive sector in advancing with cutting-edge facilities that are up to date.Citation
Rahim, M.A., Rahman, M.A., Rahman, M.M., Zaman, N., Moustafa, N. and Razzak, I. (2022) An intelligent risk management framework for monitoring vehicular engine health. IEEE Transactions on Green Communications and Networking, 10.1109/TGCN.2022.3179350Publisher
IEEEJournal
IEEE Transactions on Green Communications and NetworkingAdditional Links
https://ieeexplore.ieee.org/document/9785863Type
Journal articleLanguage
enDescription
This is an accepted manuscript of an article published by IEEE on 31/05/2022, available online: https://ieeexplore.ieee.org/document/9785863 The accepted version of the publication may differ from the final published version.ISSN
2473-2400EISSN
2473-2400ae974a485f413a2113503eed53cd6c53
10.1109/TGCN.2022.3179350
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Except where otherwise noted, this item's license is described as https://creativecommons.org/licenses/by-nc-nd/4.0/