# Fault Diagnosis Method Based on Time Series in Autonomous Unmanned System
## 1 Introduction  
The unmanned system consists of a single or multiple unmanned platforms, which can complete designated tasks autonomously or through remote control. The system is highly integrated with mechanization, information, and intelligent platforms to form a smart unmanned system, including a single unmanned aerial vehicle, unmanned vehicles, unmanned ships, unmanned submispiles, and other typical objects. In order to ensure the reliable execution of the tasks of the multi-unmanned system, it is necessary to pay attention to all aspects of the work of the multi-unmanned system and ensure the safety of each part for overall safety. The autonomous fault monitoring and fault-tolerant control of multiple unmanned systems is a hot research topic at home and abroad. Compared with the traditional single-control system, considering the scale and complexity of the multi-unmanned system, the multi-agent system lacks a central node to coordinate the overall behavior of the system. As a result, the failure of a single node can easily spread to the whole system through the coordination and cooperation between nodes, bringing severe challenges to the system security. Reference [1] designs a fault estimator that satisfies the hybrid passive/H\(\infty\) performance index for stochastic multi-agent systems with unknown actuator offset faults andpartial actuator failure faults. Reference [2] proposes a fault-tolerant tracking control design method based on LMI technology and Lyapunov stabilization method, which is applied to aircraft with sensor failures so that for disturbances and sensor failures, the closed-loop system is asymptotically stable at a given disturbance attenuation level. Reference [3] uses Kirsch operator and a deep convolutional neural network to identify abnormal areas of photovoltaic modules, extract defect features, and then use a multi-class support vector machine for defect detection. Reference [4] performs interval sampling and reconstruction of the surface vibration acceleration signals of the rotor UAV under different flight states. It then uses a one-dimensional convolutional neural network (1D-CNN) to perform the reconstruction. Fault identification can effectively identify different or weak fault types with slight differences. Reference [5] applies the transfer learning algorithm based on a convolutional neural network to the fault diagnosis of MEMS inertial sensor of unmanned aerial vehicle, improving the fault classification performance. Reference [6] proposed a credit assignment-based fuzzy cerebellar model articulation controller (FCA-CMAC) neural network information fusion model. The model is used as an estimator for unknown continuous faults, and the proposed fault identification method can diagnose thrusters' continuous, uncertain, and novel failure modes. The heading angle sensor (compass) signal, the yaw rate, and the control signal are used as the input of the FCA-CMAC, and the fault diagnosis results are obtained through offline training. Reference [7] utilizes the characteristics of parallel processing and highly self-organizing and self-learning information of a fuzzy neural network to diagnose the diesel engine of unmanned cabin, overcoming the limitation of a single system and obtaining better fault diagnosis results. Reference [8] proposes a fault diagnosis strategy for induction motors based on support vector machine (SVM) multi-classification. Fault features extracted from electrical and mechanical diagnostic media are used as input to the support vector machine, which performs feature data fusion. The above study shows that most of the existing fault intelligent diagnosis methods are diagnosis algorithms for a specific target, such as an oil cylinder, partial actuator, etc., and the algorithms are designed using its characteristics (e.g., physical characteristics, electrical characteristics). Therefore, each algorithm has different assumptions and applicable objects and lacks universality.