Joseph Chukwudi, Isinka and Zaman, Nafees and Abdur Rahim, Md and Arafatur Rahman, Md and Alenazi, Mohammed J.F. and Pillai, Prashant (2024) An ensemble deep learning model for vehicular engine health prediction. IEEE Access, 12. pp. 63433-63451. ISSN 2169-3536. (Published)
|
Pdf
An ensemble deep learning model for vehicular engine health prediction.pdf Available under License Creative Commons Attribution Non-commercial No Derivatives. Download (2MB) | Preview |
Abstract
Predictive maintenance has gained importance across various industries, including the automotive sector. It is very challenging to detect vehicle failures in advance due to the intricate composition of various components and sensors. The vehicle's reliability is of utmost importance for ensuring the absence of fatalities or malfunctions to foster economic development. This study introduces an innovative method for developing a predictive framework for vehicle engines with faster and higher decision accuracy. The framework is specifically designed to recognize patterns and abnormalities that may suggest prospective engine problems in real-time and allow proactive maintenance. We assessed the performance of the developed vehicular engine health monitoring systems using a deep learning model based on essential measures like root mean square error, root mean square deviation, mean absolute error, accuracy, confusion matrix, and area under the curve. In this case, the deep learning models are developed by following ensemble techniques using the most prominently used machine learning techniques. Significantly, Stacked Model 1 outperformed other stacked models (Models 2 and 3) and achieved an impressive AUC value of 0.9702 with a low root mean square error (RMSE) of 0.3355, a high accuracy rate of 0.9470, and a precision of 0.9486. It happens due to the effective incorporation of different approaches into Stacked Model 1, which signifies a significant advancement in predicting vehicular engine failures. The model can be used in real-time monitoring systems to continuously monitor the health of vehicular engines and provide early warnings of potential failures, thereby reducing maintenance costs and improving safety.
| Item Type: | Article |
|---|---|
| Additional Information: | Indexed by Scopus |
| Uncontrolled Keywords: | Decision strategy; Deep learning; Ensemble stacking; Machine learning; Micro services; Vehicular engine health monitoring system; Vulnerability assessment |
| Subjects: | T Technology > T Technology (General) T Technology > TA Engineering (General). Civil engineering (General) T Technology > TJ Mechanical engineering and machinery T Technology > TL Motor vehicles. Aeronautics. Astronautics |
| Faculty/Division: | Institute of Postgraduate Studies Faculty of Mechanical and Automotive Engineering Technology |
| Depositing User: | Mr Muhamad Firdaus Janih@Jaini |
| Date Deposited: | 07 Jan 2025 05:07 |
| Last Modified: | 07 Jan 2025 05:07 |
| URI: | http://umpir.ump.edu.my/id/eprint/42955 |
| Download Statistic: | View Download Statistics |
Actions (login required)
 |
View Item |