Feature Extraction and Selection Scheme for Intelligent Engine Fault Diagnosis Based on 2DNMF, Mutual Information, and NSGA-II
A novel feature extraction and selection scheme is presented for intelligent engine fault diagnosis by utilizing two-dimensional nonnegative matrix factorization (2DNMF), mutual information, and nondominated sorting genetic algorithms II (NSGA-II). Experiments are conducted on an engine test rig, in...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2016-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2016/3975285 |
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| _version_ | 1832563776384139264 |
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| author | Peng-yuan Liu Bing Li Cui-e Han Feng Wang |
| author_facet | Peng-yuan Liu Bing Li Cui-e Han Feng Wang |
| author_sort | Peng-yuan Liu |
| collection | DOAJ |
| description | A novel feature extraction and selection scheme is presented for intelligent engine fault diagnosis by utilizing two-dimensional nonnegative matrix factorization (2DNMF), mutual information, and nondominated sorting genetic algorithms II (NSGA-II). Experiments are conducted on an engine test rig, in which eight different engine operating conditions including one normal condition and seven fault conditions are simulated, to evaluate the presented feature extraction and selection scheme. In the phase of feature extraction, the S transform technique is firstly utilized to convert the engine vibration signals to time-frequency domain, which can provide richer information on engine operating conditions. Then a novel feature extraction technique, named two-dimensional nonnegative matrix factorization, is employed for characterizing the time-frequency representations. In the feature selection phase, a hybrid filter and wrapper scheme based on mutual information and NSGA-II is utilized to acquire a compact feature subset for engine fault diagnosis. Experimental results by adopted three different classifiers have demonstrated that the proposed feature extraction and selection scheme can achieve a very satisfying classification performance with fewer features for engine fault diagnosis. |
| format | Article |
| id | doaj-art-5eeb1549aa46496b9408012489c8b8b7 |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2016-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-5eeb1549aa46496b9408012489c8b8b72025-02-03T01:12:32ZengWileyShock and Vibration1070-96221875-92032016-01-01201610.1155/2016/39752853975285Feature Extraction and Selection Scheme for Intelligent Engine Fault Diagnosis Based on 2DNMF, Mutual Information, and NSGA-IIPeng-yuan Liu0Bing Li1Cui-e Han2Feng Wang3Forth Department, Mechanical Engineering College, No. 97, Heping West Road, Shijiazhuang, Hebei 050003, ChinaForth Department, Mechanical Engineering College, No. 97, Heping West Road, Shijiazhuang, Hebei 050003, ChinaForth Department, Mechanical Engineering College, No. 97, Heping West Road, Shijiazhuang, Hebei 050003, ChinaForth Department, Mechanical Engineering College, No. 97, Heping West Road, Shijiazhuang, Hebei 050003, ChinaA novel feature extraction and selection scheme is presented for intelligent engine fault diagnosis by utilizing two-dimensional nonnegative matrix factorization (2DNMF), mutual information, and nondominated sorting genetic algorithms II (NSGA-II). Experiments are conducted on an engine test rig, in which eight different engine operating conditions including one normal condition and seven fault conditions are simulated, to evaluate the presented feature extraction and selection scheme. In the phase of feature extraction, the S transform technique is firstly utilized to convert the engine vibration signals to time-frequency domain, which can provide richer information on engine operating conditions. Then a novel feature extraction technique, named two-dimensional nonnegative matrix factorization, is employed for characterizing the time-frequency representations. In the feature selection phase, a hybrid filter and wrapper scheme based on mutual information and NSGA-II is utilized to acquire a compact feature subset for engine fault diagnosis. Experimental results by adopted three different classifiers have demonstrated that the proposed feature extraction and selection scheme can achieve a very satisfying classification performance with fewer features for engine fault diagnosis.http://dx.doi.org/10.1155/2016/3975285 |
| spellingShingle | Peng-yuan Liu Bing Li Cui-e Han Feng Wang Feature Extraction and Selection Scheme for Intelligent Engine Fault Diagnosis Based on 2DNMF, Mutual Information, and NSGA-II Shock and Vibration |
| title | Feature Extraction and Selection Scheme for Intelligent Engine Fault Diagnosis Based on 2DNMF, Mutual Information, and NSGA-II |
| title_full | Feature Extraction and Selection Scheme for Intelligent Engine Fault Diagnosis Based on 2DNMF, Mutual Information, and NSGA-II |
| title_fullStr | Feature Extraction and Selection Scheme for Intelligent Engine Fault Diagnosis Based on 2DNMF, Mutual Information, and NSGA-II |
| title_full_unstemmed | Feature Extraction and Selection Scheme for Intelligent Engine Fault Diagnosis Based on 2DNMF, Mutual Information, and NSGA-II |
| title_short | Feature Extraction and Selection Scheme for Intelligent Engine Fault Diagnosis Based on 2DNMF, Mutual Information, and NSGA-II |
| title_sort | feature extraction and selection scheme for intelligent engine fault diagnosis based on 2dnmf mutual information and nsga ii |
| url | http://dx.doi.org/10.1155/2016/3975285 |
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