Research on Feature Extraction Method of Engine Misfire Fault Based on Signal Sparse Decomposition
Engine vibration signals are easy to be interfered by other noise, causing feature signals that represent its operating status get submerged and further leading to difficulty in engine fault diagnosis. In addition, most of the signals utilized to verify the extraction method are derived from numeric...
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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/10.1155/2021/6650932 |
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| _version_ | 1850176331966316544 |
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| author | Canyi Du Fei Jiang Kang Ding Feng Li Feifei Yu |
| author_facet | Canyi Du Fei Jiang Kang Ding Feng Li Feifei Yu |
| author_sort | Canyi Du |
| collection | DOAJ |
| description | Engine vibration signals are easy to be interfered by other noise, causing feature signals that represent its operating status get submerged and further leading to difficulty in engine fault diagnosis. In addition, most of the signals utilized to verify the extraction method are derived from numerical simulation, which are far away from the real engine signals. To address these problems, this paper combines the priority of signal sparse decomposition and engine finite element model to research a novel feature extraction method for engine misfire diagnosis. Firstly, in order to highlight resonance regions related with impact features, the vibration signal is performed with a high-pass filter process. Secondly, the dictionary with clear physical meaning is constructed by the unit impulse function, whose parameters are associated with engine system modal characteristics. Afterwards, the signals that indicate the engine operating status are accurately reconstructed by segmental matching pursuit. Finally, a series of precise simulation signals originated from the engine dynamic finite element model, and experimental signals on the automotive engine are used to verify the proposed method’s effectiveness and antinoise performance. Additionally, comparisons with wavelet decomposition further show the proposed method to be more reliable in engine misfire diagnosis. |
| format | Article |
| id | doaj-art-c6801e949fce43c18f539ca42cd3c1bc |
| institution | OA Journals |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-c6801e949fce43c18f539ca42cd3c1bc2025-08-20T02:19:16ZengWileyShock and Vibration1070-96221875-92032021-01-01202110.1155/2021/66509326650932Research on Feature Extraction Method of Engine Misfire Fault Based on Signal Sparse DecompositionCanyi Du0Fei Jiang1Kang Ding2Feng Li3Feifei Yu4School of Automobile and Transportation Engineering, Guangdong Polytechnic Normal University, Guangzhou 510450, ChinaSchool of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510640, ChinaSchool of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510640, ChinaSchool of Automobile and Transportation Engineering, Guangdong Polytechnic Normal University, Guangzhou 510450, ChinaSchool of Automobile and Transportation Engineering, Guangdong Polytechnic Normal University, Guangzhou 510450, ChinaEngine vibration signals are easy to be interfered by other noise, causing feature signals that represent its operating status get submerged and further leading to difficulty in engine fault diagnosis. In addition, most of the signals utilized to verify the extraction method are derived from numerical simulation, which are far away from the real engine signals. To address these problems, this paper combines the priority of signal sparse decomposition and engine finite element model to research a novel feature extraction method for engine misfire diagnosis. Firstly, in order to highlight resonance regions related with impact features, the vibration signal is performed with a high-pass filter process. Secondly, the dictionary with clear physical meaning is constructed by the unit impulse function, whose parameters are associated with engine system modal characteristics. Afterwards, the signals that indicate the engine operating status are accurately reconstructed by segmental matching pursuit. Finally, a series of precise simulation signals originated from the engine dynamic finite element model, and experimental signals on the automotive engine are used to verify the proposed method’s effectiveness and antinoise performance. Additionally, comparisons with wavelet decomposition further show the proposed method to be more reliable in engine misfire diagnosis.http://dx.doi.org/10.1155/2021/6650932 |
| spellingShingle | Canyi Du Fei Jiang Kang Ding Feng Li Feifei Yu Research on Feature Extraction Method of Engine Misfire Fault Based on Signal Sparse Decomposition Shock and Vibration |
| title | Research on Feature Extraction Method of Engine Misfire Fault Based on Signal Sparse Decomposition |
| title_full | Research on Feature Extraction Method of Engine Misfire Fault Based on Signal Sparse Decomposition |
| title_fullStr | Research on Feature Extraction Method of Engine Misfire Fault Based on Signal Sparse Decomposition |
| title_full_unstemmed | Research on Feature Extraction Method of Engine Misfire Fault Based on Signal Sparse Decomposition |
| title_short | Research on Feature Extraction Method of Engine Misfire Fault Based on Signal Sparse Decomposition |
| title_sort | research on feature extraction method of engine misfire fault based on signal sparse decomposition |
| url | http://dx.doi.org/10.1155/2021/6650932 |
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