Anomaly Detection and Condition Monitoring of UAV Motors and Propellers

Pourpanah, Farhad; Zhang, Bin; Ma, Rui; Hao, Qi

10.1109/ICSENS.2018.8589572

2018

21689229

978-1-5386-4708-0

2018 IEEE SENSORS

2018-October

184-187

28-31 Oct. 2018

New Delhi, India

345 E 47TH ST, NEW YORK, NY 10017 USA

IEEE

An early detection of fault components is crucial for unmanned aerial vehicles (UAVs). The goal of this paper is to develop a monitoring system to early detect possible faults of UAV motors and propellers. Motor current signature analysis (MCSA) approach is used to analyze the stator current signals under different conditions. Then, fuzzy adaptive resonance (Fuzzy ART) neural network (NN), which is an unsupervised learning scheme, is employed to judge whether motors are operating in normal or faulty condition. In addition, the vibration signature analysis (VSA) technique is employed to monitor the UAV propellers. A Q-learning-based Fuzzy ARTMAP NN is used to learn extracted statistical features, and the Genetic algorithm (GA) is used to select an optimal subset of features through an off-line manner in order to reduce computational time. The experimental results validated the effectiveness of the proposed model in detecting faults of UAV motors and propellers as compared with CART, KNN, NB and SVM.

Propellers Feature extraction Unmanned aerial vehicles Monitoring Fault detection Subspace constraints Vibrations

第一 ; 通讯

英语

CPCI-S ; EI

National Natural Science Foundation of China[61773197]

Engineering ; Remote Sensing

Engineering, Electrical & Electronic ; Remote Sensing

WOS:000468199300048

20190606463828

Aircraft detection ; Antennas ; Arts computing ; Condition monitoring ; Fault detection ; Genetic algorithms ; Propellers ; Unmanned aerial vehicles (UAV) ; Vibration analysis

Aircraft, General:652.1 ; Radar Systems and Equipment:716.2 ; Data Processing and Image Processing:723.2

Web of Science

Southern Univ Sci & Technol, Sch Comp Sci & Engn, Shenzhen 518055, Peoples R China

Pourpanah, Farhad,Zhang, Bin,Ma, Rui,et al. Anomaly Detection and Condition Monitoring of UAV Motors and Propellers[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2018:184-187.