# Online Learning Based Underwater Robotic Thruster Fault Detection
###### Abstract
Similar to underwater robotics, the fault detection of unmanned aerial vehicle (UAV) is susceptible to the influences of gusts and turbulence and model complexity. Considerable researches have been conducted for UAV fault detection, which also can be divided into model-based methods and data-driven methods. In model-based approaches, relations between measurements and estimated states are exploited to detect possible faults. The most common ways to implement a model-based approach is to estimate the states, to estimate the model parameters, or parity-space. In general, the model-based methods mainly integrate with Kalman filter, particle filter or their variants by establishing a physical model as the state transition equation [21]. A fault detection method based on the suboptimal fading unscented Kalman filter (SFUKF) is designed in [22]. [PERSON] et al. [23] introduced an adaptive estimation method based on a bank of unscented Kalman filters (UKF) to monitor the actuator health in a UAV. In the literature [24], [PERSON] et al. utilized Extended Kalman Filter (EKF) and the UAV kinematics model to realize analytical redundancy. [PERSON] et al. [25] developed a detection strategy, in which the weighting parameters are updated by EKF, to find faults in sensors and actuators. In [26], [PERSON] and [PERSON] presented a model-based fault detection method based on particle filter. In data-driven approaches, historical flight data are utilized for training the models. Once the new flight data are inconsistent with the pattern learned by the trained model, a fault alarm is given. Amidst data driven approaches for the fault detection of UAV, machine learning methods such as artificial neural networks [27] and support vector machines (SVM) appear more recently in the literature [28; 29; 30]. [PERSON] et al. [30] proposed a classification method based on principal component analysis (PCA) and SVM to detect faults of UAV control surfaces. [PERSON] et al. [31] proposed an optimized one-class SVM method regulated by local density to realize fault detection for UAV. A fault detection method based on a stacked long short-term memory (LSTM) neural network is designed in literature [32], where the LSTM model is used to realize accurate prediction of the monitored parameter, and fault detection is achieved via a statistical threshold of the smoothed prediction residuals. In [33], a data-driven multivariate regression approach based on long short-term memory with residual filtering (LSTM-RF) is proposed to fulfill UAV fault detection and recovery. In order to perform fault detection in the presence of ocean current interference and real-time state changes, we propose an online learning-based fault detection method for underwater robotic thruster systems. First, a control-variable-speed model of the thruster is built online, from which the propeller speed is predicted. Then, when the residual of the model exceeds a threshold value due to sea current interference, the operation state will change and the model will be changed by an online update mechanism. Eventually, the fault detection model will be triggered. In order to improve the accuracy of online modeling, the time delay between control variables and speed is estimated using correlation analysis, such that the modeling data are realigned in accordance with the time delay estimation results. Secondly, a multi-center particle swarm optimization algorithm with short-term memory merit is designed, which combines the prior knowledge and swarm intelligence to analyze the model in the modeling process. Our fault detection method is based on the thruster control-variable-speed model. Therefore, it has the ability to adapt to interference factors, such as the influence of the ocean current and the change of robot operation states. Neither an accurate mathematical model of the underwater vehicle nor complete training data are required in our method. The rest of the paper is organized as follows. In Section 3, the online estimation of time delay in relation to the thruster system is described.