Zhang Xiaomin, Zhao Zhiyao, Wang Zhaoyang, Wang Xiaoyi
School of Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China.
China Light Industry Key Laboratory of Industrial Internet and Big Data, Beijing Technology and Business University, Beijing 100048, China.
Sensors (Basel). 2021 Jan 15;21(2):581. doi: 10.3390/s21020581.
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.
四轴飞行器广泛应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飞行器安全至关重要。现有的数据驱动故障检测方法通常需要大量传感器来收集数据。然而，四轴飞行器机身空间有限，无法加载大量传感器，这意味着难以使用额外的传感器来捕获四轴飞行器的故障信号。本文提出了一种基于机身振动信号的四轴飞行器叶片故障检测与识别（FDI）方法，该方法不使用额外的传感器，而是利用机载加速度传感器。该方法整合了多轴数据信息，并通过长短时记忆（LSTM）网络模型有效地检测和识别四轴飞行器叶片故障。通过飞行实验，首先采集四轴飞行器三轴加速度计数据作为机身振动信号。然后，采用小波包分解方法提取数据特征，并利用小波包系数的标准差形成特征向量。最后，构建基于LSTM的FDI模型用于四轴飞行器叶片的FDI。结果表明，与基于反向传播（BP）神经网络的FDI模型相比，该方法能够有效地检测和识别四轴飞行器叶片故障，具有更好的FDI性能和更高的模型精度。
