Bio-Inspired Fault Diagnosis for Aircraft Fuel Pumps Using a Cloud-Edge System
The fuel pump serves as the central component of the aircraft fuel system, necessitating real-time data acquisition for monitoring purposes. As the number of sensors increases, there is a substantial rise in data volume, leading to a simultaneous increase in computational processing for traditional...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-12-01
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| Series: | Biomimetics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2313-7673/8/8/601 |
| _version_ | 1797381874990972928 |
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| author | Yang Miao Yantang Li Jun Pan Zhen Liu Lei Liu Zeng Wang Zijing Wang |
| author_facet | Yang Miao Yantang Li Jun Pan Zhen Liu Lei Liu Zeng Wang Zijing Wang |
| author_sort | Yang Miao |
| collection | DOAJ |
| description | The fuel pump serves as the central component of the aircraft fuel system, necessitating real-time data acquisition for monitoring purposes. As the number of sensors increases, there is a substantial rise in data volume, leading to a simultaneous increase in computational processing for traditional Prognostics and Health Management methods while computational efficiency decreases. In response to this challenge, a novel health monitoring approach for aircraft fuel pumps is proposed based on the collaborative utilization of cloud-edge resources. This approach enables efficient cooperation among the sensor side, edge side, and cloud side to achieve timely fault warnings and accurate fault classification for fuel pumps. Within this method, anomaly judgment tasks are allocated to the edge side, and an anomaly judgment method that integrates the 3σ threshold and “3/5 strategy” is devised. Additionally, a fault diagnosis algorithm, founded on a convolutional auto-encoder, is formulated in the cloud to discern various fault types and severities. Comparative results demonstrate that, in contrast to long short-term memory networks, convolutional neural networks, extreme learning machines, and support vector machines, the proposed method yields improvements in accuracy of 4.35%, 6.40%, 17.65%, and 19.35%, respectively. Consequently, it is evident that the proposed method exhibits notable efficacy in the condition monitoring of aircraft fuel pumps. |
| first_indexed | 2024-03-08T20:57:49Z |
| format | Article |
| id | doaj.art-868117bfe1f1403c930fd751759ea576 |
| institution | Directory Open Access Journal |
| issn | 2313-7673 |
| language | English |
| last_indexed | 2024-03-08T20:57:49Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomimetics |
| spelling | doaj.art-868117bfe1f1403c930fd751759ea5762023-12-22T13:55:38ZengMDPI AGBiomimetics2313-76732023-12-018860110.3390/biomimetics8080601Bio-Inspired Fault Diagnosis for Aircraft Fuel Pumps Using a Cloud-Edge SystemYang Miao0Yantang Li1Jun Pan2Zhen Liu3Lei Liu4Zeng Wang5Zijing Wang6Faculty of Materials and Manufacturing, Beijing University of Technology, Beijing 100124, ChinaFaculty of Materials and Manufacturing, Beijing University of Technology, Beijing 100124, ChinaAVIC Nanjing Electromechanical Hydraulic Engineering Center, Nanjing 211102, ChinaFaculty of Materials and Manufacturing, Beijing University of Technology, Beijing 100124, ChinaLand Space Technology Huzhou Co., Ltd., Huzhou 313099, ChinaChina Aerospace Science and Technology Corporation, Beijing 100076, ChinaBeijing Institute of Radio Measurement, Beijing 100143, ChinaThe fuel pump serves as the central component of the aircraft fuel system, necessitating real-time data acquisition for monitoring purposes. As the number of sensors increases, there is a substantial rise in data volume, leading to a simultaneous increase in computational processing for traditional Prognostics and Health Management methods while computational efficiency decreases. In response to this challenge, a novel health monitoring approach for aircraft fuel pumps is proposed based on the collaborative utilization of cloud-edge resources. This approach enables efficient cooperation among the sensor side, edge side, and cloud side to achieve timely fault warnings and accurate fault classification for fuel pumps. Within this method, anomaly judgment tasks are allocated to the edge side, and an anomaly judgment method that integrates the 3σ threshold and “3/5 strategy” is devised. Additionally, a fault diagnosis algorithm, founded on a convolutional auto-encoder, is formulated in the cloud to discern various fault types and severities. Comparative results demonstrate that, in contrast to long short-term memory networks, convolutional neural networks, extreme learning machines, and support vector machines, the proposed method yields improvements in accuracy of 4.35%, 6.40%, 17.65%, and 19.35%, respectively. Consequently, it is evident that the proposed method exhibits notable efficacy in the condition monitoring of aircraft fuel pumps.https://www.mdpi.com/2313-7673/8/8/601aircraft fuel pumphealth monitoringcloud-edge collaboration3/5 strategyfault diagnosis |
| spellingShingle | Yang Miao Yantang Li Jun Pan Zhen Liu Lei Liu Zeng Wang Zijing Wang Bio-Inspired Fault Diagnosis for Aircraft Fuel Pumps Using a Cloud-Edge System Biomimetics aircraft fuel pump health monitoring cloud-edge collaboration 3/5 strategy fault diagnosis |
| title | Bio-Inspired Fault Diagnosis for Aircraft Fuel Pumps Using a Cloud-Edge System |
| title_full | Bio-Inspired Fault Diagnosis for Aircraft Fuel Pumps Using a Cloud-Edge System |
| title_fullStr | Bio-Inspired Fault Diagnosis for Aircraft Fuel Pumps Using a Cloud-Edge System |
| title_full_unstemmed | Bio-Inspired Fault Diagnosis for Aircraft Fuel Pumps Using a Cloud-Edge System |
| title_short | Bio-Inspired Fault Diagnosis for Aircraft Fuel Pumps Using a Cloud-Edge System |
| title_sort | bio inspired fault diagnosis for aircraft fuel pumps using a cloud edge system |
| topic | aircraft fuel pump health monitoring cloud-edge collaboration 3/5 strategy fault diagnosis |
| url | https://www.mdpi.com/2313-7673/8/8/601 |
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