Due to the extensive use of Unmanned Aerial Vehicles (UAVs) and the co-evolution of current technology, a key introduction to fault detection has arisen in recent studies in order to prevent unfortunate consequences. In this study, vibration-based signals from a commercially available innovative quadcopter flying in hover mode are collected using a vibration accelerometer, a data acquisition device, and a laptop. An ADXL335 accelerometer is fixed on the center of the drone where the centerlines of the four blades intersect. The superposition of numerous vibration arrangements over identical spectra hinders the ability to analyze the spectral data in the manner required to locate any framework's discrete vibration. This work presents a technique for separating a synthesized vibration signal towards discrete vibrations and other extraneous vibrations of a structure utilizing the Discrete Wavelet Transform (DWT) integrated with the Fast Fourier Transform (FFT). The research article findings in this study demonstrate the reliability and applicability of specific categories of discrete vibrations that are sorted out during the structural change evaluation to develop the best feasible strategy for removing the undesired and unanticipated vibration components and noise. The methodology demonstrated in this paper has the potential for practical application to multirotor UAVs in general.

由于无人机（UAV）的广泛使用和当前技术的共同进化，最近的研究中出现了故障检测的关键介绍，以防止不幸的后果。在这项研究中，使用振动加速度计、数据采集设备和笔记本电脑收集来自以悬停模式飞行的市售创新四轴飞行器的基于振动的信号。ADXL335加速度计固定在无人机的中心，四个桨叶中心线相交的地方。相同频谱上众多振动排列的叠加阻碍了以定位任何框架的离散振动所需的方式分析频谱数据的能力。这项工作提出了一种利用离散小波变换 (DWT) 与快速傅里叶变换 (FFT) 集成的技术，将合成振动信号分离为结构的离散振动和其他无关振动。本研究中的研究文章结果证明了在结构变化评估过程中整理出的特定类别离散振动的可靠性和适用性，以开发消除不需要和意外的振动分量和噪声的最佳可行策略。本文演示的方法具有一般多旋翼无人机实际应用的潜力。