Intelligent Fault Diagnosis of Aeroengine Sensors Using Improved Pattern Gradient Spectrum Entropy
Timely and effective fault diagnosis of sensors is crucial to enhance the working efficiency and reliability of the aeroengine. A new intelligent fault diagnosis scheme combining improved pattern gradient spectrum entropy (IPGSE) and convolutional neural network (CNN) is proposed in this paper, aimi...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | International Journal of Aerospace Engineering |
| Online Access: | http://dx.doi.org/10.1155/2021/8868875 |
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| _version_ | 1832561283634823168 |
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| author | Huihui Li Linfeng Gou Hua Zheng Huacong Li |
| author_facet | Huihui Li Linfeng Gou Hua Zheng Huacong Li |
| author_sort | Huihui Li |
| collection | DOAJ |
| description | Timely and effective fault diagnosis of sensors is crucial to enhance the working efficiency and reliability of the aeroengine. A new intelligent fault diagnosis scheme combining improved pattern gradient spectrum entropy (IPGSE) and convolutional neural network (CNN) is proposed in this paper, aiming at the problem of poor fault diagnosis effect and real-time performance when CNN directly processes one-dimensional time series signals of aeroengine. Firstly, raw fault signals are converted into spectral entropy images by introducing pattern gradient spectral entropy (PGSE), which is used as the input of CNN, because of the great advantage of CNN in processing images and the simple and rapid calculation of the modal gradient spectral entropy. The simulation results prove that IPGSE has more stable distinguishing characteristics. Then, we improved PGSE to use particle swarm optimization algorithm to adaptively optimize the influencing parameters (scale factor λ), so that the obtained spectral entropy graph can better match the CNN. Finally, CNN mode is proposed to classify the spectral entropy diagram. The method is validated with datasets containing different fault types. The experimental results show that this method can be easily applied to the online automatic fault diagnosis of aeroengine control system sensors. |
| format | Article |
| id | doaj-art-582fc0bae1ae4a6c92a3d532e645c089 |
| institution | Kabale University |
| issn | 1687-5966 1687-5974 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Aerospace Engineering |
| spelling | doaj-art-582fc0bae1ae4a6c92a3d532e645c0892025-02-03T01:25:25ZengWileyInternational Journal of Aerospace Engineering1687-59661687-59742021-01-01202110.1155/2021/88688758868875Intelligent Fault Diagnosis of Aeroengine Sensors Using Improved Pattern Gradient Spectrum EntropyHuihui Li0Linfeng Gou1Hua Zheng2Huacong Li3School of Power and Energy, Northwestern Polytechnical University, Xi’an 710072, ChinaSchool of Power and Energy, Northwestern Polytechnical University, Xi’an 710072, ChinaSchool of Power and Energy, Northwestern Polytechnical University, Xi’an 710072, ChinaSchool of Power and Energy, Northwestern Polytechnical University, Xi’an 710072, ChinaTimely and effective fault diagnosis of sensors is crucial to enhance the working efficiency and reliability of the aeroengine. A new intelligent fault diagnosis scheme combining improved pattern gradient spectrum entropy (IPGSE) and convolutional neural network (CNN) is proposed in this paper, aiming at the problem of poor fault diagnosis effect and real-time performance when CNN directly processes one-dimensional time series signals of aeroengine. Firstly, raw fault signals are converted into spectral entropy images by introducing pattern gradient spectral entropy (PGSE), which is used as the input of CNN, because of the great advantage of CNN in processing images and the simple and rapid calculation of the modal gradient spectral entropy. The simulation results prove that IPGSE has more stable distinguishing characteristics. Then, we improved PGSE to use particle swarm optimization algorithm to adaptively optimize the influencing parameters (scale factor λ), so that the obtained spectral entropy graph can better match the CNN. Finally, CNN mode is proposed to classify the spectral entropy diagram. The method is validated with datasets containing different fault types. The experimental results show that this method can be easily applied to the online automatic fault diagnosis of aeroengine control system sensors.http://dx.doi.org/10.1155/2021/8868875 |
| spellingShingle | Huihui Li Linfeng Gou Hua Zheng Huacong Li Intelligent Fault Diagnosis of Aeroengine Sensors Using Improved Pattern Gradient Spectrum Entropy International Journal of Aerospace Engineering |
| title | Intelligent Fault Diagnosis of Aeroengine Sensors Using Improved Pattern Gradient Spectrum Entropy |
| title_full | Intelligent Fault Diagnosis of Aeroengine Sensors Using Improved Pattern Gradient Spectrum Entropy |
| title_fullStr | Intelligent Fault Diagnosis of Aeroengine Sensors Using Improved Pattern Gradient Spectrum Entropy |
| title_full_unstemmed | Intelligent Fault Diagnosis of Aeroengine Sensors Using Improved Pattern Gradient Spectrum Entropy |
| title_short | Intelligent Fault Diagnosis of Aeroengine Sensors Using Improved Pattern Gradient Spectrum Entropy |
| title_sort | intelligent fault diagnosis of aeroengine sensors using improved pattern gradient spectrum entropy |
| url | http://dx.doi.org/10.1155/2021/8868875 |
| work_keys_str_mv | AT huihuili intelligentfaultdiagnosisofaeroenginesensorsusingimprovedpatterngradientspectrumentropy AT linfenggou intelligentfaultdiagnosisofaeroenginesensorsusingimprovedpatterngradientspectrumentropy AT huazheng intelligentfaultdiagnosisofaeroenginesensorsusingimprovedpatterngradientspectrumentropy AT huacongli intelligentfaultdiagnosisofaeroenginesensorsusingimprovedpatterngradientspectrumentropy |