Misfire Fault Diagnosis Method for Diesel Engine Based on MEMD and Dispersion Entropy
As a main source of power, diesel engines are widely used in large mechanical systems. Fire failure is a kind of common fault condition, which seriously affects the power and economy of the diesel engine. Previously, scholars mostly used single-channel signal to diagnose the misfire fault of the die...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2021/9213697 |
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| _version_ | 1832550169509363712 |
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| author | Cheng Gu Xin-Yong Qiao Huaying Li Ying Jin |
| author_facet | Cheng Gu Xin-Yong Qiao Huaying Li Ying Jin |
| author_sort | Cheng Gu |
| collection | DOAJ |
| description | As a main source of power, diesel engines are widely used in large mechanical systems. Fire failure is a kind of common fault condition, which seriously affects the power and economy of the diesel engine. Previously, scholars mostly used single-channel signal to diagnose the misfire fault of the diesel engine. However, the single-channel signal has limitations in reflecting the information of fault. A novel fault diagnosis method based on MEMD and dispersion entropy is proposed in this paper. Firstly, the multichannel vibration signal of the diesel engine cylinder head is decomposed by multivariate empirical mode decomposition (MEMD), which obtains the IMF component groups with the same frequency in the same order. Then, the IMF component with a large correlation coefficient with the original signal in each group is selected to reconstruct new signal, and dispersion entropy (DE) of the reconstructed signal is calculated as a fault feature vector. Finally, the fault feature vector is input into the support vector machine (SVM) for misfire fault classification. Compared with the other three methods, the results show that the diagnosis method proposed in this paper can effectively extract the fault features and accurately identify the fault type, which is superior to the comparison method. |
| format | Article |
| id | doaj-art-d5db0743d469415b9f634fbc30feee7a |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-d5db0743d469415b9f634fbc30feee7a2025-02-03T06:07:38ZengWileyShock and Vibration1070-96221875-92032021-01-01202110.1155/2021/92136979213697Misfire Fault Diagnosis Method for Diesel Engine Based on MEMD and Dispersion EntropyCheng Gu0Xin-Yong Qiao1Huaying Li2Ying Jin3Department of Vehicle Engineering, Academy of Army Armored Forces, Beijing, ChinaDepartment of Vehicle Engineering, Academy of Army Armored Forces, Beijing, ChinaDepartment of Vehicle Engineering, Academy of Army Armored Forces, Beijing, ChinaDepartment of Vehicle Engineering, Academy of Army Armored Forces, Beijing, ChinaAs a main source of power, diesel engines are widely used in large mechanical systems. Fire failure is a kind of common fault condition, which seriously affects the power and economy of the diesel engine. Previously, scholars mostly used single-channel signal to diagnose the misfire fault of the diesel engine. However, the single-channel signal has limitations in reflecting the information of fault. A novel fault diagnosis method based on MEMD and dispersion entropy is proposed in this paper. Firstly, the multichannel vibration signal of the diesel engine cylinder head is decomposed by multivariate empirical mode decomposition (MEMD), which obtains the IMF component groups with the same frequency in the same order. Then, the IMF component with a large correlation coefficient with the original signal in each group is selected to reconstruct new signal, and dispersion entropy (DE) of the reconstructed signal is calculated as a fault feature vector. Finally, the fault feature vector is input into the support vector machine (SVM) for misfire fault classification. Compared with the other three methods, the results show that the diagnosis method proposed in this paper can effectively extract the fault features and accurately identify the fault type, which is superior to the comparison method.http://dx.doi.org/10.1155/2021/9213697 |
| spellingShingle | Cheng Gu Xin-Yong Qiao Huaying Li Ying Jin Misfire Fault Diagnosis Method for Diesel Engine Based on MEMD and Dispersion Entropy Shock and Vibration |
| title | Misfire Fault Diagnosis Method for Diesel Engine Based on MEMD and Dispersion Entropy |
| title_full | Misfire Fault Diagnosis Method for Diesel Engine Based on MEMD and Dispersion Entropy |
| title_fullStr | Misfire Fault Diagnosis Method for Diesel Engine Based on MEMD and Dispersion Entropy |
| title_full_unstemmed | Misfire Fault Diagnosis Method for Diesel Engine Based on MEMD and Dispersion Entropy |
| title_short | Misfire Fault Diagnosis Method for Diesel Engine Based on MEMD and Dispersion Entropy |
| title_sort | misfire fault diagnosis method for diesel engine based on memd and dispersion entropy |
| url | http://dx.doi.org/10.1155/2021/9213697 |
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