An ensemble deep learning model for vehicular engine health prediction
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 ensurin...
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| Format: | Article |
| Language: | English |
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Institute of Electrical and Electronics Engineers Inc.
2024
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| Online Access: | http://umpir.ump.edu.my/id/eprint/42955/1/An%20ensemble%20deep%20learning%20model%20for%20vehicular%20engine%20health%20prediction.pdf |
| _version_ | 1824451653890211840 |
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| author | Joseph Chukwudi, Isinka Zaman, Nafees Abdur Rahim, Md Arafatur Rahman, Md Alenazi, Mohammed J.F. Pillai, Prashant |
| author_facet | Joseph Chukwudi, Isinka Zaman, Nafees Abdur Rahim, Md Arafatur Rahman, Md Alenazi, Mohammed J.F. Pillai, Prashant |
| author_sort | Joseph Chukwudi, Isinka |
| collection | UMP |
| description | 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. |
| first_indexed | 2025-02-19T02:38:03Z |
| format | Article |
| id | UMPir42955 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2025-02-19T02:38:03Z |
| publishDate | 2024 |
| publisher | Institute of Electrical and Electronics Engineers Inc. |
| record_format | dspace |
| spelling | UMPir429552025-01-07T05:07:13Z http://umpir.ump.edu.my/id/eprint/42955/ An ensemble deep learning model for vehicular engine health prediction Joseph Chukwudi, Isinka Zaman, Nafees Abdur Rahim, Md Arafatur Rahman, Md Alenazi, Mohammed J.F. Pillai, Prashant T Technology (General) TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery TL Motor vehicles. Aeronautics. Astronautics 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. Institute of Electrical and Electronics Engineers Inc. 2024 Article PeerReviewed pdf en cc_by_nc_nd_4 http://umpir.ump.edu.my/id/eprint/42955/1/An%20ensemble%20deep%20learning%20model%20for%20vehicular%20engine%20health%20prediction.pdf 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) https://doi.org/10.1109/ACCESS.2024.3395927 https://doi.org/10.1109/ACCESS.2024.3395927 |
| spellingShingle | T Technology (General) TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery TL Motor vehicles. Aeronautics. Astronautics Joseph Chukwudi, Isinka Zaman, Nafees Abdur Rahim, Md Arafatur Rahman, Md Alenazi, Mohammed J.F. Pillai, Prashant An ensemble deep learning model for vehicular engine health prediction |
| title | An ensemble deep learning model for vehicular engine health prediction |
| title_full | An ensemble deep learning model for vehicular engine health prediction |
| title_fullStr | An ensemble deep learning model for vehicular engine health prediction |
| title_full_unstemmed | An ensemble deep learning model for vehicular engine health prediction |
| title_short | An ensemble deep learning model for vehicular engine health prediction |
| title_sort | ensemble deep learning model for vehicular engine health prediction |
| topic | T Technology (General) TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery TL Motor vehicles. Aeronautics. Astronautics |
| url | http://umpir.ump.edu.my/id/eprint/42955/1/An%20ensemble%20deep%20learning%20model%20for%20vehicular%20engine%20health%20prediction.pdf |
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