The growing interest in unmanned aerial vehicles (UAVs) from both the scientific and industrial sectors has attracted a wave of new researchers and substantial investments in this expansive field. However, due to the wide range of topics and subdomains within UAV research, newcomers may find themselves overwhelmed by the numerous options available. It is therefore crucial for those involved in UAV research to recognize its interdisciplinary nature and its connections with other disciplines. This paper presents a comprehensive overview of the UAV field, highlighting recent trends and advancements. Drawing on recent literature reviews and surveys, the review begins by classifying UAVs based on their flight characteristics. It then provides an overview of current research trends in UAVs, utilizing data from the Scopus database to quantify the number of scientific documents associated with each research direction and their interconnections. This paper also explores potential areas for further development in UAVs, including communication, artificial intelligence, remote sensing, miniaturization, swarming and cooperative control, and transformability. Additionally, it discusses the development of aircraft control, commonly used control techniques, and appropriate control algorithms in UAV research. Furthermore, this paper addresses the general hardware and software architecture of UAVs, their applications, and the key issues associated with them. It also provides an overview of current open source software and hardware projects in the UAV field. By presenting a comprehensive view of the UAV field, this paper aims to enhance our understanding of this rapidly evolving and highly interdisciplinary area of research.

The increasing prevalence of unmanned aerial vehicles (UAVs) across various fields requires the development of advanced fault detection and diagnostic (FDD) frameworks to prevent the severe consequences of undetected sensor and actuator failures. This review investigates the wide spectrum of FDD methodologies for UAVs, focusing on the paramount role of sophisticated yet intelligent systems in safeguarding operational integrity, particularly in near-human environments. An analysis of 32 seminal publications from well-recognized databases presents a trend towards converging signal processing and machine learning techniques using UAV specific fault detection keywords. This analysis underscores the trend of data-driven models capable of performing real-time diagnostics. The authors increase interest in hybrid methodologies that correlate the precision of signal processing and the adaptive nature of machine learning. These approaches aim to gain fault detection accuracy and achieve better prognostic capabilities. By deconstructing the strengths and weaknesses of various methods, this analysis concludes with the need for further research into upcoming new challenges. The review focuses on the investigation of synergistic strategies and encourages interdisciplinary collaboration for advancements in FDD methods. These initiatives will enhance UAV safety and reliability across a wide range of operational contexts.

This paper presents two diagnosis techniques currently very much answered by the analysis of the signature of the stator current and the analysis of vibration signal because they are primarily. These techniques are based on the motor current signature analysis (MCSA). The MCSA method was based on analyzing the Fast Fourier Transform (FFT) of the stator current to analyze the spectral content of the different signals. The precision of these techniques depends on the load and speed of the induction machine. The diagnosis and detection of the presence of static eccentricity faults by various techniques mentioned above, and it was performed analytically and practically. The purpose of this article is to compare two methods: analysis of the stator current (MCSA-FFT) and the vibration analysis method (MVSA-FFT) to access a valuable decision.