Aero Engine Fault Diagnosis Using an Optimized Extreme Learning Machine
A new extreme learning machine optimized by quantum-behaved particle swarm optimization (QPSO) is developed in this paper. It uses QPSO to select optimal network parameters including the number of hidden layer neurons according to both the root mean square error on validation data set and the norm o...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2016-01-01
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| Series: | International Journal of Aerospace Engineering |
| Online Access: | http://dx.doi.org/10.1155/2016/7892875 |
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| _version_ | 1850237504166297600 |
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| author | Xinyi Yang Shan Pang Wei Shen Xuesen Lin Keyi Jiang Yonghua Wang |
| author_facet | Xinyi Yang Shan Pang Wei Shen Xuesen Lin Keyi Jiang Yonghua Wang |
| author_sort | Xinyi Yang |
| collection | DOAJ |
| description | A new extreme learning machine optimized by quantum-behaved particle swarm optimization (QPSO) is developed in this paper. It uses QPSO to select optimal network parameters including the number of hidden layer neurons according to both the root mean square error on validation data set and the norm of output weights. The proposed Q-ELM was applied to real-world classification applications and a gas turbine fan engine diagnostic problem and was compared with two other optimized ELM methods and original ELM, SVM, and BP method. Results show that the proposed Q-ELM is a more reliable and suitable method than conventional neural network and other ELM methods for the defect diagnosis of the gas turbine engine. |
| format | Article |
| id | doaj-art-1cbd90cdbc8f4d03bcbfea6aff051b4a |
| institution | OA Journals |
| issn | 1687-5966 1687-5974 |
| language | English |
| publishDate | 2016-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Aerospace Engineering |
| spelling | doaj-art-1cbd90cdbc8f4d03bcbfea6aff051b4a2025-08-20T02:01:43ZengWileyInternational Journal of Aerospace Engineering1687-59661687-59742016-01-01201610.1155/2016/78928757892875Aero Engine Fault Diagnosis Using an Optimized Extreme Learning MachineXinyi Yang0Shan Pang1Wei Shen2Xuesen Lin3Keyi Jiang4Yonghua Wang5Department of Aerocraft Engineering, Naval Aeronautical and Astronautical University, Yantai 264001, ChinaCollege of Information and Electrical Engineering, Ludong University, Yantai 264025, ChinaDepartment of Aerocraft Engineering, Naval Aeronautical and Astronautical University, Yantai 264001, ChinaDepartment of Aerocraft Engineering, Naval Aeronautical and Astronautical University, Yantai 264001, ChinaDepartment of Aerocraft Engineering, Naval Aeronautical and Astronautical University, Yantai 264001, ChinaDepartment of Aerocraft Engineering, Naval Aeronautical and Astronautical University, Yantai 264001, ChinaA new extreme learning machine optimized by quantum-behaved particle swarm optimization (QPSO) is developed in this paper. It uses QPSO to select optimal network parameters including the number of hidden layer neurons according to both the root mean square error on validation data set and the norm of output weights. The proposed Q-ELM was applied to real-world classification applications and a gas turbine fan engine diagnostic problem and was compared with two other optimized ELM methods and original ELM, SVM, and BP method. Results show that the proposed Q-ELM is a more reliable and suitable method than conventional neural network and other ELM methods for the defect diagnosis of the gas turbine engine.http://dx.doi.org/10.1155/2016/7892875 |
| spellingShingle | Xinyi Yang Shan Pang Wei Shen Xuesen Lin Keyi Jiang Yonghua Wang Aero Engine Fault Diagnosis Using an Optimized Extreme Learning Machine International Journal of Aerospace Engineering |
| title | Aero Engine Fault Diagnosis Using an Optimized Extreme Learning Machine |
| title_full | Aero Engine Fault Diagnosis Using an Optimized Extreme Learning Machine |
| title_fullStr | Aero Engine Fault Diagnosis Using an Optimized Extreme Learning Machine |
| title_full_unstemmed | Aero Engine Fault Diagnosis Using an Optimized Extreme Learning Machine |
| title_short | Aero Engine Fault Diagnosis Using an Optimized Extreme Learning Machine |
| title_sort | aero engine fault diagnosis using an optimized extreme learning machine |
| url | http://dx.doi.org/10.1155/2016/7892875 |
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