Quadcopters are widely used in a variety of military and civilian mission scenarios. Real-time online detection of the abnormal state of the quadcopter is vital to the safety of aircraft. Existing data-driven fault detection methods generally usually require numerous sensors to collect data. However, quadcopter airframe space is limited. A large number of sensors cannot be loaded, meaning that it is difficult to use additional sensors to capture fault signals for quadcopters. In this paper, without additional sensors, a Fault Detection and Identification (FDI) method for quadcopter blades based on airframe vibration signals is proposed using the airborne acceleration sensor. This method integrates multi-axis data information and effectively detects and identifies quadcopter blade faults through Long and Short-Term Memory (LSTM) network models. Through flight experiments, the quadcopter triaxial accelerometer data are collected for airframe vibration signals at first. Then, the wavelet packet decomposition method is employed to extract data features, and the standard deviations of the wavelet packet coefficients are employed to form the feature vector. Finally, the LSTM-based FDI model is constructed for quadcopter blade FDI. The results show that the method can effectively detect and identify quadcopter blade faults with a better FDI performance and a higher model accuracy compared with the Back Propagation (BP) neural network-based FDI model.

中文翻译：

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机体振动信号的四旋翼飞机故障检测与识别方法

四旋翼飞行器广泛用于各种军事和民用任务场景。实时在线检测四轴飞行器的异常状态对于飞机的安全至关重要。现有的数据驱动的故障检测方法通常通常需要大量的传感器来收集数据。但是，四轴飞行器的机身空间有限。无法加载大量传感器，这意味着很难使用其他传感器来捕获四轴飞行器的故障信号。在没有附加传感器的情况下，提出了一种使用机载加速度传感器的基于机身振动信号的四旋翼飞机叶片故障检测与识别（FDI）方法。该方法集成了多轴数据信息，并通过长期和短期记忆（LSTM）网络模型有效地检测和识别了四旋翼飞机叶片故障。通过飞行实验，首先收集四轴飞行器三轴加速度计数据以获取机身振动信号。然后，采用小波包分解方法提取数据特征，并采用小波包系数的标准差形成特征向量。最后，针对四旋翼叶片FDI构建了基于LSTM的FDI模型。结果表明，与基于BP神经网络的FDI模型相比，该方法可以有效地检测和识别四旋翼叶片故障，具有更好的FDI性能和更高的模型精度。利用小波包系数的标准差形成特征向量。最后，针对四旋翼叶片FDI构建了基于LSTM的FDI模型。结果表明，与基于BP神经网络的FDI模型相比，该方法可以有效地检测和识别四旋翼叶片故障，具有更好的FDI性能和更高的模型精度。利用小波包系数的标准差形成特征向量。最后，针对四旋翼叶片FDI构建了基于LSTM的FDI模型。结果表明，与基于BP神经网络的FDI模型相比，该方法可以有效地检测和识别四旋翼叶片故障，具有更好的FDI性能和更高的模型精度。