Combustion fault detection technique of spark ignition engine based on wavelet packet transform and artificial neural network
In the present work, the wavelet packet technique based on the vibration signals is proposed under normal and fault conditions of the spark ignition (SI) engine. A novelty fault diagnosis technique is considered through the calculation of the maximum energy to Shannon entropy ratio for twenty-five m...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2020-10-01
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| Series: | Alexandria Engineering Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S111001682030291X |
| _version_ | 1818669513047015424 |
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| author | M.A. Hashim M.H. Nasef A.E. Kabeel Nouby M. Ghazaly |
| author_facet | M.A. Hashim M.H. Nasef A.E. Kabeel Nouby M. Ghazaly |
| author_sort | M.A. Hashim |
| collection | DOAJ |
| description | In the present work, the wavelet packet technique based on the vibration signals is proposed under normal and fault conditions of the spark ignition (SI) engine. A novelty fault diagnosis technique is considered through the calculation of the maximum energy to Shannon entropy ratio for twenty-five mother wavelets. An optimization approach is conducted for selecting the wavelets and decomposition level to reduce the noise of the captured signal. Feature extraction based on a discrete wavelet transform and energy spectrum is extracted. Effect of the selection of proper de-noising wavelet on the performance of both supervised and unsupervised artificial neural network (ANN) is evaluated. Experimental results show that Coif2_2, dmey_2, and rbio5.5_2 are valuable wavelets for de-noising signal of the SI engine. It is also found that the maximum energy to Shannon entropy ratio is a fast and powerful method to be used in the selection of wavelet families with the best decomposition level. In addition, it indicated that the wavelet packet transform has great potential in detecting spark plug defects. It can be reported that the de-noising with the wavelet revealed the best results on the performance of the ANN for the classification and clustering of fault or normal states. |
| first_indexed | 2024-12-17T06:53:24Z |
| format | Article |
| id | doaj.art-752e4a525d3743d0857893dcf0c67fb1 |
| institution | Directory Open Access Journal |
| issn | 1110-0168 |
| language | English |
| last_indexed | 2024-12-17T06:53:24Z |
| publishDate | 2020-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj.art-752e4a525d3743d0857893dcf0c67fb12022-12-21T21:59:32ZengElsevierAlexandria Engineering Journal1110-01682020-10-0159536873697Combustion fault detection technique of spark ignition engine based on wavelet packet transform and artificial neural networkM.A. Hashim0M.H. Nasef1A.E. Kabeel2Nouby M. Ghazaly3Mechanical and Electrical Research Institute, National Water Research Center, Cairo 12622, EgyptMechanical Eng. Dept., Faculty of Engineering, Fayoum University, Fayoum 63514, EgyptMechanical Power Engineering Department, Faculty of Engineering, Tanta University, Tanta, Egypt; Corresponding author.Mechanical Eng. Dept., Faculty of Engineering, South Valley University, Qena 83523, EgyptIn the present work, the wavelet packet technique based on the vibration signals is proposed under normal and fault conditions of the spark ignition (SI) engine. A novelty fault diagnosis technique is considered through the calculation of the maximum energy to Shannon entropy ratio for twenty-five mother wavelets. An optimization approach is conducted for selecting the wavelets and decomposition level to reduce the noise of the captured signal. Feature extraction based on a discrete wavelet transform and energy spectrum is extracted. Effect of the selection of proper de-noising wavelet on the performance of both supervised and unsupervised artificial neural network (ANN) is evaluated. Experimental results show that Coif2_2, dmey_2, and rbio5.5_2 are valuable wavelets for de-noising signal of the SI engine. It is also found that the maximum energy to Shannon entropy ratio is a fast and powerful method to be used in the selection of wavelet families with the best decomposition level. In addition, it indicated that the wavelet packet transform has great potential in detecting spark plug defects. It can be reported that the de-noising with the wavelet revealed the best results on the performance of the ANN for the classification and clustering of fault or normal states.http://www.sciencedirect.com/science/article/pii/S111001682030291XInternal combustion enginesWavelet packet transform Fault diagnosisFeature extractionSupervised and unsupervised learningArtificial Neural Networks (ANN) |
| spellingShingle | M.A. Hashim M.H. Nasef A.E. Kabeel Nouby M. Ghazaly Combustion fault detection technique of spark ignition engine based on wavelet packet transform and artificial neural network Alexandria Engineering Journal Internal combustion engines Wavelet packet transform Fault diagnosis Feature extraction Supervised and unsupervised learning Artificial Neural Networks (ANN) |
| title | Combustion fault detection technique of spark ignition engine based on wavelet packet transform and artificial neural network |
| title_full | Combustion fault detection technique of spark ignition engine based on wavelet packet transform and artificial neural network |
| title_fullStr | Combustion fault detection technique of spark ignition engine based on wavelet packet transform and artificial neural network |
| title_full_unstemmed | Combustion fault detection technique of spark ignition engine based on wavelet packet transform and artificial neural network |
| title_short | Combustion fault detection technique of spark ignition engine based on wavelet packet transform and artificial neural network |
| title_sort | combustion fault detection technique of spark ignition engine based on wavelet packet transform and artificial neural network |
| topic | Internal combustion engines Wavelet packet transform Fault diagnosis Feature extraction Supervised and unsupervised learning Artificial Neural Networks (ANN) |
| url | http://www.sciencedirect.com/science/article/pii/S111001682030291X |
| work_keys_str_mv | AT mahashim combustionfaultdetectiontechniqueofsparkignitionenginebasedonwaveletpackettransformandartificialneuralnetwork AT mhnasef combustionfaultdetectiontechniqueofsparkignitionenginebasedonwaveletpackettransformandartificialneuralnetwork AT aekabeel combustionfaultdetectiontechniqueofsparkignitionenginebasedonwaveletpackettransformandartificialneuralnetwork AT noubymghazaly combustionfaultdetectiontechniqueofsparkignitionenginebasedonwaveletpackettransformandartificialneuralnetwork |