Small Sample Fault Diagnosis Based on Intrinsic Characteristic Encouraged Network for Unmanned Aerial Vehicle Sensors

Fault detection and classification (FDC) of industrial sensors are commonly featured with rare fault cases and demanding diagnostic accuracy. Data-driven diagnostic strategies including signal processing and neural networks prove to be more prominent compared to traditional ways. Most data-driven st...

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Veröffentlicht in:	IEEE transactions on instrumentation and measurement Jg. 73; S. 1 - 14
Hauptverfasser:	Song, Jia, Wu, Boxuan, Zhao, Chaoyue, Shang, Weize, Liu, Yang, Li, Wenling
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Accelerometers Accuracy Algorithms Autonomous aerial vehicles Data models Encoders-Decoders Fault detection Fault diagnosis Gyroscopes gyroscopes and accelerometers Kinematics Navigation neural network Neural networks Sensor phenomena and characterization Sensors Signal processing small samples unmanned aerial vehicle (UAV) Unmanned aerial vehicles
ISSN:	0018-9456, 1557-9662
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Abstract	Fault detection and classification (FDC) of industrial sensors are commonly featured with rare fault cases and demanding diagnostic accuracy. Data-driven diagnostic strategies including signal processing and neural networks prove to be more prominent compared to traditional ways. Most data-driven studies on fault diagnosis pay less attention to small sample situations, which potentially erodes the practical application value. In this article, an intrinsic characteristic encouraged network (ICEN) is structured to address the aforementioned problem in the area of unmanned aerial vehicle (UAV) sensor fault diagnosis. The model of UAV sensor output with multiple fault types is realized for the set-up of elaborate small sample datasets. Specifically, gyroscopes and accelerometers are chosen as the subject sensors, and the generated samples are grouped accordingly. The designed ICEN framework incorporates the signal processing algorithm into a boosted encoder-decoder structure to strengthen the feature compactness of samples and achieve the fault diagnosis with limited data. After parameter optimization, the ICEN framework is trained and tested depending on the variety of sensors. Through comparative analysis of both software and hardware platforms, it is demonstrated that the proposed ICEN framework claims higher accuracy and faster speed in the field of FDC of UAV sensors when faced with small sample situations.
AbstractList	Fault detection and classification (FDC) of industrial sensors are commonly featured with rare fault cases and demanding diagnostic accuracy. Data-driven diagnostic strategies including signal processing and neural networks prove to be more prominent compared to traditional ways. Most data-driven studies on fault diagnosis pay less attention to small sample situations, which potentially erodes the practical application value. In this article, an intrinsic characteristic encouraged network (ICEN) is structured to address the aforementioned problem in the area of unmanned aerial vehicle (UAV) sensor fault diagnosis. The model of UAV sensor output with multiple fault types is realized for the set-up of elaborate small sample datasets. Specifically, gyroscopes and accelerometers are chosen as the subject sensors, and the generated samples are grouped accordingly. The designed ICEN framework incorporates the signal processing algorithm into a boosted encoder-decoder structure to strengthen the feature compactness of samples and achieve the fault diagnosis with limited data. After parameter optimization, the ICEN framework is trained and tested depending on the variety of sensors. Through comparative analysis of both software and hardware platforms, it is demonstrated that the proposed ICEN framework claims higher accuracy and faster speed in the field of FDC of UAV sensors when faced with small sample situations.
Author	Li, Wenling Shang, Weize Wu, Boxuan Liu, Yang Zhao, Chaoyue Song, Jia
Author_xml	– sequence: 1 givenname: Jia orcidid: 0000-0002-4019-970X surname: Song fullname: Song, Jia email: songjia@buaa.edu.cn organization: School of Astronautics, Beihang University, Beijing, China – sequence: 2 givenname: Boxuan orcidid: 0009-0008-0479-4887 surname: Wu fullname: Wu, Boxuan email: boxuanwu@buaa.edu.cn organization: School of Astronautics, Beihang University, Beijing, China – sequence: 3 givenname: Chaoyue orcidid: 0009-0009-7377-625X surname: Zhao fullname: Zhao, Chaoyue email: zhaocy@buaa.edu.cn organization: School of Astronautics, Beihang University, Beijing, China – sequence: 4 givenname: Weize orcidid: 0000-0003-0241-7494 surname: Shang fullname: Shang, Weize email: shangwz0918@163.com organization: Shenyang Aircraft Design and Research Institute, Aviation Industry Corporation of China, Shenyang, China – sequence: 5 givenname: Yang orcidid: 0000-0002-0139-9573 surname: Liu fullname: Liu, Yang email: ylbuaa@163.com organization: School of Automation Science and Electrical Engineering, Beihang University, Beijing, China – sequence: 6 givenname: Wenling orcidid: 0000-0002-9130-5736 surname: Li fullname: Li, Wenling email: lwlmath@buaa.edu.cn organization: School of Automation Science and Electrical Engineering, Beihang University, Beijing, China
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SubjectTerms	Accelerometers Accuracy Algorithms Autonomous aerial vehicles Data models Encoders-Decoders Fault detection Fault diagnosis Gyroscopes gyroscopes and accelerometers Kinematics Navigation neural network Neural networks Sensor phenomena and characterization Sensors Signal processing small samples unmanned aerial vehicle (UAV) Unmanned aerial vehicles
Title	Small Sample Fault Diagnosis Based on Intrinsic Characteristic Encouraged Network for Unmanned Aerial Vehicle Sensors
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