Engine fault diagnosis using probabilistic neural network
Engine failure is one of the major factors caused vehicle breakdown. In the current practice, the engine faults are diagnosed manually by mechanics and the accuracy is highly relied on their experience. Therefore, this study would like to explore the feasibility of implementing auto fault diagnosis...
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| Format: | Proceedings |
| Language: | English English |
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Institute of Electrical and Electronics Engineers
2021
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| Subjects: | |
| Online Access: | https://eprints.ums.edu.my/id/eprint/32537/1/Engine%20fault%20diagnosis%20using%20probabilistic%20neural%20network.ABSTRACT.pdf https://eprints.ums.edu.my/id/eprint/32537/2/Engine%20fault%20diagnosis%20using%20probabilistic%20neural%20network.pdf |
| _version_ | 1825714711531880448 |
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| author | Sheng, Zhu Min, Keng Tan Ka, Renee Yin Chin Bih, Lii Chua Xiaoxi, Hao Tze, Kenneth Kin Teo |
| author_facet | Sheng, Zhu Min, Keng Tan Ka, Renee Yin Chin Bih, Lii Chua Xiaoxi, Hao Tze, Kenneth Kin Teo |
| author_sort | Sheng, Zhu |
| collection | UMS |
| description | Engine failure is one of the major factors caused vehicle breakdown. In the current practice, the engine faults are diagnosed manually by mechanics and the accuracy is highly relied on their experience. Therefore, this study would like to explore the feasibility of implementing auto fault diagnosis using Probabilistic Neural Network (PNN). A benchmarked engine fault model is developed and simulated in Maltab. The proposed algorithm is designed to detect 9 common engine faults based on the information extracted from exhaust gas, such as hydrocarbon (HC), carbon monoxide (CO), oxides of nitrogen (NOx), carbon dioxide (CO2) and dioxygen (O2). The proposed PNN is trained using the collected engine fault data from experiment and the probability density of PNN is determined based on the Parzen window estimation method. Bayes decision rule is implemented for classifying the types of the engine faults. The simulated results show that the proposed algorithm has faster diagnosis speed, higher accuracy and consistent. The algorithm takes 0.038 s in diagnosing the fault and the average accuracy is 98.3 %. |
| first_indexed | 2024-03-06T03:15:52Z |
| format | Proceedings |
| id | ums.eprints-32537 |
| institution | Universiti Malaysia Sabah |
| language | English English |
| last_indexed | 2024-03-06T03:15:52Z |
| publishDate | 2021 |
| publisher | Institute of Electrical and Electronics Engineers |
| record_format | dspace |
| spelling | ums.eprints-325372022-05-03T14:18:50Z https://eprints.ums.edu.my/id/eprint/32537/ Engine fault diagnosis using probabilistic neural network Sheng, Zhu Min, Keng Tan Ka, Renee Yin Chin Bih, Lii Chua Xiaoxi, Hao Tze, Kenneth Kin Teo TL1-4050 Motor vehicles. Aeronautics. Astronautics Engine failure is one of the major factors caused vehicle breakdown. In the current practice, the engine faults are diagnosed manually by mechanics and the accuracy is highly relied on their experience. Therefore, this study would like to explore the feasibility of implementing auto fault diagnosis using Probabilistic Neural Network (PNN). A benchmarked engine fault model is developed and simulated in Maltab. The proposed algorithm is designed to detect 9 common engine faults based on the information extracted from exhaust gas, such as hydrocarbon (HC), carbon monoxide (CO), oxides of nitrogen (NOx), carbon dioxide (CO2) and dioxygen (O2). The proposed PNN is trained using the collected engine fault data from experiment and the probability density of PNN is determined based on the Parzen window estimation method. Bayes decision rule is implemented for classifying the types of the engine faults. The simulated results show that the proposed algorithm has faster diagnosis speed, higher accuracy and consistent. The algorithm takes 0.038 s in diagnosing the fault and the average accuracy is 98.3 %. Institute of Electrical and Electronics Engineers 2021-10-28 Proceedings PeerReviewed text en https://eprints.ums.edu.my/id/eprint/32537/1/Engine%20fault%20diagnosis%20using%20probabilistic%20neural%20network.ABSTRACT.pdf text en https://eprints.ums.edu.my/id/eprint/32537/2/Engine%20fault%20diagnosis%20using%20probabilistic%20neural%20network.pdf Sheng, Zhu and Min, Keng Tan and Ka, Renee Yin Chin and Bih, Lii Chua and Xiaoxi, Hao and Tze, Kenneth Kin Teo (2021) Engine fault diagnosis using probabilistic neural network. https://ieeexplore.ieee.org/document/9573654 |
| spellingShingle | TL1-4050 Motor vehicles. Aeronautics. Astronautics Sheng, Zhu Min, Keng Tan Ka, Renee Yin Chin Bih, Lii Chua Xiaoxi, Hao Tze, Kenneth Kin Teo Engine fault diagnosis using probabilistic neural network |
| title | Engine fault diagnosis using probabilistic neural network |
| title_full | Engine fault diagnosis using probabilistic neural network |
| title_fullStr | Engine fault diagnosis using probabilistic neural network |
| title_full_unstemmed | Engine fault diagnosis using probabilistic neural network |
| title_short | Engine fault diagnosis using probabilistic neural network |
| title_sort | engine fault diagnosis using probabilistic neural network |
| topic | TL1-4050 Motor vehicles. Aeronautics. Astronautics |
| url | https://eprints.ums.edu.my/id/eprint/32537/1/Engine%20fault%20diagnosis%20using%20probabilistic%20neural%20network.ABSTRACT.pdf https://eprints.ums.edu.my/id/eprint/32537/2/Engine%20fault%20diagnosis%20using%20probabilistic%20neural%20network.pdf |
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