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A comprehensive review of electrochemical hybrid power supply systems and intelligent energy managements for unmanned aerial vehicles in public services 被引量:2
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作者 Caizhi Zhang Yuqi Qiu +5 位作者 Jiawei Chen Yuehua Li Zhitao Liu Yang Liu Jiujun Zhang Chan Siew Hwa 《Energy and AI》 2022年第3期148-171,共24页
The electric unmanned aerial vehicles (UAVs) are rapidly growing due to their abilities to perform some difficult or dangerous tasks as well as many public services including real-time monitoring, wireless coverage, s... The electric unmanned aerial vehicles (UAVs) are rapidly growing due to their abilities to perform some difficult or dangerous tasks as well as many public services including real-time monitoring, wireless coverage, search and rescue, wildlife surveys, and precision agriculture. However, the electrochemical power supply system of UAV is a critical issue in terms of its energy/power densities and lifetime for service endurance. In this paper, the current power supply systems used in UAVs are comprehensively reviewed and analyzed on the existing power configurations and the energy management systems. It is identified that a single type of electrochemical power source is not enough to support a UAV to achieve a long-haul flight;hence, a hybrid power system architecture is necessary. To make use of the advantages of each type of power source to increase the endurance and achieve good performance of the UAVs, the hybrid systems containing two or three types of power sources (fuel cell,battery, solar cell, and supercapacitor,) have to be developed. In this regard, the selection of an appropriate hybrid power structure with the optimized energy management system is critical for the efficient operation of a UAV. It is found that the data-driven models with artificial intelligence (AI) are promising in intelligent energy management. This paper can provide insights and guidelines for future research and development into the design and fabrication of the advanced UAV power systems. 展开更多
关键词 Unmanned aerial vehicles(UAVs) Power supply system Fuel cell system Artificial intelligence(AI) Energy management systems
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A Novel Rolling Bearing Vibration Impulsive Signals Detection Approach Based on Dictionary Learning 被引量:2
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作者 Chuan Sun Hongpeng Yin +1 位作者 Yanxia Li Yi Chai 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2021年第6期1188-1198,共11页
The localized faults of rolling bearings can be diagnosed by its vibration impulsive signals.However,it is always a challenge to extract the impulsive feature under background noise and non-stationary conditions.This ... The localized faults of rolling bearings can be diagnosed by its vibration impulsive signals.However,it is always a challenge to extract the impulsive feature under background noise and non-stationary conditions.This paper investigates impulsive signals detection of a single-point defect rolling bearing and presents a novel data-driven detection approach based on dictionary learning.To overcome the effects harmonic and noise components,we propose an autoregressive-minimum entropy deconvolution model to separate harmonic and deconvolve the effect of the transmission path.To address the shortcomings of conventional sparse representation under the changeable operation environment,we propose an approach that combines K-clustering with singular value decomposition(K-SVD)and split-Bregman to extract impulsive components precisely.Via experiments on synthetic signals and real run-to-failure signals,the excellent performance for different impulsive signals detection verifies the effectiveness and robustness of the proposed approach.Meanwhile,a comparison with the state-of-the-art methods is illustrated,which shows that the proposed approach can provide more accurate detected impulsive signals. 展开更多
关键词 Dictionary learning impulsive signals detection Kclustering with singular value decomposition(K-SVD) minimum entropy deconvolution rolling bearing signal processing
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