A Compound Fault Labeling and Diagnosis Method Based on Flight Data and BIT Record of UAV

• Main Authors: Ke Zheng, Guozhu Jia, Linchao Yang, Jiaqing Wang
• Format: Article
• Language: English
• Published: MDPI AG 2021-06-01
• Series: Applied Sciences
• Subjects: fault diagnosis; data labeling; UAV; flight data and BIT record; machine learning
• Online Access: https://www.mdpi.com/2076-3417/11/12/5410

In the process of Unmanned Aerial Vehicle (UAV) flight testing, plenty of compound faults exist, which could be composed of concurrent single faults or over-limit states alarmed by Built-In-Test (BIT) equipment. At present, there still lacks a suitable automatic labeling approach for UAV flight data, effectively utilizing the information of the BIT record. The performance of the originally employed flight data-driven fault diagnosis models based on machine learning needs to be improved as well. A compound fault labeling and diagnosis method based on actual flight data and the BIT record of the UAV during flight test phase is proposed, through labeling the flight data with compound fault modes corresponding to concurrent single faults recorded by the BIT system, and upgrading the original diagnosis model based on Gradient Boosting Decision Tree (GBDT) and Fully Convolutional Network (FCNN), to eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM) and modified Convolutional Neural Network (CNN). The experimental results based on actual test flight data show that the proposed method could effectively label the flight data and obtain a significant improvement in diagnostic performance, appearing to be practical in the UAV test flight process.