Insulation condition forewarning of form‐wound winding for electric aircraft propulsion based on partial discharge and deep learning neural network
Abstract Form‐wound windings in electric machines designed for electric aircraft propulsion face reliability challenges due to the severe operating environment, such as high temperature and low pressure. This study proposes a forewarning method for insulation condition monitoring of form‐wound windi...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-04-01
|
| Series: | High Voltage |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/hve2.12034 |
| _version_ | 1798018300380184576 |
|---|---|
| author | Yalin Wang Jiandong Wu Tao Han Kiruba Haran Yi Yin |
| author_facet | Yalin Wang Jiandong Wu Tao Han Kiruba Haran Yi Yin |
| author_sort | Yalin Wang |
| collection | DOAJ |
| description | Abstract Form‐wound windings in electric machines designed for electric aircraft propulsion face reliability challenges due to the severe operating environment, such as high temperature and low pressure. This study proposes a forewarning method for insulation condition monitoring of form‐wound windings based on partial discharge (PD) and deep learning neural network. Three PD features are extracted from the PD profile, which provides information about physics‐of‐failure and reflects the degree of insulation degradation. An algorithm fusion extracted from auto‐encoder and long short‐term recurrent neural network is proposed to synthesize one failure precursor from these three features and make multi‐time‐step prediction through historical data to provide forewarning. An electrical and thermal accelerated ageing test is performed on the form‐wound windings at 0.2 atm to simulate working environment of electric aircraft. The proposed method is validated on the accelerated ageing dataset and shows better prediction accuracy than some existing time‐series prediction methods, indicating the advantages of the proposed method. Moreover, an on‐line hardware setup using a deep learning processor is recommended to implement the forewarning method. The proposed approach has the potential to be widely applied to other insulation systems and contribute to work on condition monitoring. |
| first_indexed | 2024-04-11T16:21:48Z |
| format | Article |
| id | doaj.art-0b8f961fc20144ad99b112b82a059df5 |
| institution | Directory Open Access Journal |
| issn | 2397-7264 |
| language | English |
| last_indexed | 2024-04-11T16:21:48Z |
| publishDate | 2021-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | High Voltage |
| spelling | doaj.art-0b8f961fc20144ad99b112b82a059df52022-12-22T04:14:18ZengWileyHigh Voltage2397-72642021-04-016230231310.1049/hve2.12034Insulation condition forewarning of form‐wound winding for electric aircraft propulsion based on partial discharge and deep learning neural networkYalin Wang0Jiandong Wu1Tao Han2Kiruba Haran3Yi Yin4Department of Electrical Engineering Shanghai Jiao Tong University Shanghai ChinaDepartment of Electrical Engineering Shanghai Jiao Tong University Shanghai ChinaDepartment of Instrument Science and Engineering Shanghai Jiao Tong University Shanghai ChinaDepartment of Electrical and Computer Engineering University of Illinois Urbana‐Champaign Urbana Illinois USADepartment of Electrical Engineering Shanghai Jiao Tong University Shanghai ChinaAbstract Form‐wound windings in electric machines designed for electric aircraft propulsion face reliability challenges due to the severe operating environment, such as high temperature and low pressure. This study proposes a forewarning method for insulation condition monitoring of form‐wound windings based on partial discharge (PD) and deep learning neural network. Three PD features are extracted from the PD profile, which provides information about physics‐of‐failure and reflects the degree of insulation degradation. An algorithm fusion extracted from auto‐encoder and long short‐term recurrent neural network is proposed to synthesize one failure precursor from these three features and make multi‐time‐step prediction through historical data to provide forewarning. An electrical and thermal accelerated ageing test is performed on the form‐wound windings at 0.2 atm to simulate working environment of electric aircraft. The proposed method is validated on the accelerated ageing dataset and shows better prediction accuracy than some existing time‐series prediction methods, indicating the advantages of the proposed method. Moreover, an on‐line hardware setup using a deep learning processor is recommended to implement the forewarning method. The proposed approach has the potential to be widely applied to other insulation systems and contribute to work on condition monitoring.https://doi.org/10.1049/hve2.12034aerospace propulsionageingaircraftcondition monitoringelectric machineslearning (artificial intelligence) |
| spellingShingle | Yalin Wang Jiandong Wu Tao Han Kiruba Haran Yi Yin Insulation condition forewarning of form‐wound winding for electric aircraft propulsion based on partial discharge and deep learning neural network High Voltage aerospace propulsion ageing aircraft condition monitoring electric machines learning (artificial intelligence) |
| title | Insulation condition forewarning of form‐wound winding for electric aircraft propulsion based on partial discharge and deep learning neural network |
| title_full | Insulation condition forewarning of form‐wound winding for electric aircraft propulsion based on partial discharge and deep learning neural network |
| title_fullStr | Insulation condition forewarning of form‐wound winding for electric aircraft propulsion based on partial discharge and deep learning neural network |
| title_full_unstemmed | Insulation condition forewarning of form‐wound winding for electric aircraft propulsion based on partial discharge and deep learning neural network |
| title_short | Insulation condition forewarning of form‐wound winding for electric aircraft propulsion based on partial discharge and deep learning neural network |
| title_sort | insulation condition forewarning of form wound winding for electric aircraft propulsion based on partial discharge and deep learning neural network |
| topic | aerospace propulsion ageing aircraft condition monitoring electric machines learning (artificial intelligence) |
| url | https://doi.org/10.1049/hve2.12034 |
| work_keys_str_mv | AT yalinwang insulationconditionforewarningofformwoundwindingforelectricaircraftpropulsionbasedonpartialdischargeanddeeplearningneuralnetwork AT jiandongwu insulationconditionforewarningofformwoundwindingforelectricaircraftpropulsionbasedonpartialdischargeanddeeplearningneuralnetwork AT taohan insulationconditionforewarningofformwoundwindingforelectricaircraftpropulsionbasedonpartialdischargeanddeeplearningneuralnetwork AT kirubaharan insulationconditionforewarningofformwoundwindingforelectricaircraftpropulsionbasedonpartialdischargeanddeeplearningneuralnetwork AT yiyin insulationconditionforewarningofformwoundwindingforelectricaircraftpropulsionbasedonpartialdischargeanddeeplearningneuralnetwork |