Progressive Improved Convolutional Neural Network for Avionics Fault Diagnosis
Among deep learning methods, convolutional neural networks (CNNs) are able to extract features automatically and have increasingly been used in intelligent fault diagnosis studies. However, studies seldomly concentrate on the weakness associated with a highly imbalanced distribution of fault types d...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8905998/ |
| _version_ | 1818556861060743168 |
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| author | Shuwen Chen Hongjuan Ge Jing Li Michael Pecht |
| author_facet | Shuwen Chen Hongjuan Ge Jing Li Michael Pecht |
| author_sort | Shuwen Chen |
| collection | DOAJ |
| description | Among deep learning methods, convolutional neural networks (CNNs) are able to extract features automatically and have increasingly been used in intelligent fault diagnosis studies. However, studies seldomly concentrate on the weakness associated with a highly imbalanced distribution of fault types due to different failure rates and when multiple faults are easily confused with single faults. To solve these problems, this paper developed a stochastic discrete-time series deep convolutional neural network (SDCNN) method based on random oversampling along with a progressive method with multiple SDCNNs to improve the diagnosis performance. To assess the developed method, datasets from three avionics 24-pulse auto-transformer rectifier units (ATRUs), which are secondary electric power supplies in aircraft, were analyzed and compared with other CNN methods. |
| first_indexed | 2024-12-13T23:52:41Z |
| format | Article |
| id | doaj.art-b1e24802dc9341fcb1bb9310265bf3c8 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-13T23:52:41Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-b1e24802dc9341fcb1bb9310265bf3c82022-12-21T23:26:43ZengIEEEIEEE Access2169-35362019-01-01717736217737510.1109/ACCESS.2019.29541708905998Progressive Improved Convolutional Neural Network for Avionics Fault DiagnosisShuwen Chen0https://orcid.org/0000-0003-1286-6891Hongjuan Ge1Jing Li2Michael Pecht3College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing, CO, ChinaCollege of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing, CO, ChinaCollege of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing, CO, ChinaCollege of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing, CO, ChinaAmong deep learning methods, convolutional neural networks (CNNs) are able to extract features automatically and have increasingly been used in intelligent fault diagnosis studies. However, studies seldomly concentrate on the weakness associated with a highly imbalanced distribution of fault types due to different failure rates and when multiple faults are easily confused with single faults. To solve these problems, this paper developed a stochastic discrete-time series deep convolutional neural network (SDCNN) method based on random oversampling along with a progressive method with multiple SDCNNs to improve the diagnosis performance. To assess the developed method, datasets from three avionics 24-pulse auto-transformer rectifier units (ATRUs), which are secondary electric power supplies in aircraft, were analyzed and compared with other CNN methods.https://ieeexplore.ieee.org/document/8905998/Fault diagnosisconvolutional neural networkimbalanced classificationconfusable fault types |
| spellingShingle | Shuwen Chen Hongjuan Ge Jing Li Michael Pecht Progressive Improved Convolutional Neural Network for Avionics Fault Diagnosis IEEE Access Fault diagnosis convolutional neural network imbalanced classification confusable fault types |
| title | Progressive Improved Convolutional Neural Network for Avionics Fault Diagnosis |
| title_full | Progressive Improved Convolutional Neural Network for Avionics Fault Diagnosis |
| title_fullStr | Progressive Improved Convolutional Neural Network for Avionics Fault Diagnosis |
| title_full_unstemmed | Progressive Improved Convolutional Neural Network for Avionics Fault Diagnosis |
| title_short | Progressive Improved Convolutional Neural Network for Avionics Fault Diagnosis |
| title_sort | progressive improved convolutional neural network for avionics fault diagnosis |
| topic | Fault diagnosis convolutional neural network imbalanced classification confusable fault types |
| url | https://ieeexplore.ieee.org/document/8905998/ |
| work_keys_str_mv | AT shuwenchen progressiveimprovedconvolutionalneuralnetworkforavionicsfaultdiagnosis AT hongjuange progressiveimprovedconvolutionalneuralnetworkforavionicsfaultdiagnosis AT jingli progressiveimprovedconvolutionalneuralnetworkforavionicsfaultdiagnosis AT michaelpecht progressiveimprovedconvolutionalneuralnetworkforavionicsfaultdiagnosis |