# Online Learning Based Underwater Robotic Thruster Fault Detection
###### Abstract  
This paper presents a novel online learning-based fault detection designed for underwater robotic thruster health monitoring. In the fault detection algorithm, we build a mathematical model between the control variable and the propeller speed by fitting collected online work status data to the model. To improve the accuracy of online modeling, a multi-center PSO algorithm with memory ability is utilized to optimize the modeling parameters. Additionally, a model online update mechanism is designed to accommodate the model to the change of thruster work status and sea environment. During the operation, propeller speed of the underwater robot is predicted through the online learning-based model, and the model residuals are used for thruster health monitoring. To avoid false alarm, an adaptive fault detection strategy is established based on model online update mechanism. The proposed method has been extensively evaluated using different underwater robotics, through a sea trial data simulation, a pool test fault detection experiment and a sea trial fault detection experiment. Compared with fixed model-based method, speed prediction MAE of the online learning model is at least 37.9% lower than that of the fixed model. The online learning-based method show no misdiagnosis in experiments, while the fixed model-based method is misdiagnosed. Experimental results show that the proposed method is competitive in terms of accuracy, adaptability, and robustness. underwater robotic; thruster system; time delay estimation; particle swarm optimization; online learning; adaptive fault detection +
Footnote †: journal: Medical-speed sensors [6; 7; 8]. Through the use of such sensing modalities, the underwater robot is able to collect important measurements, such as body velocity and heading attitude. The parameters related to robotics faults can be estimated, in an online manner, by analyzing the thruster control variables. Unfortunately, the underwater robot moving states generally deviate from the expected values in the presence of thruster faults. Some researchers have found that thruster faults can be detected by comparing the residuals between the expected values and the real measurements (i.e., a fault is determined to occur if the residuals exceed specific thresholds). These are the basic principles of designing underwater robotics fault detection systems. Representative methods can be categorized into three types: analytical model-based [9; 10], filtering-based [7; 11; 12], and data-driven approach-based [13; 14; 15]. These methods have been applied to several underwater fault detection scenarios, thus promoting the development of underwater robotics. Limited by the underwater conditions and applications, underwater robotics fault detection still suffers from the following issues:  
(1) Most fault detection methods are based on robotic moving state abnormality; however, other factors, such as sea current, also affect the robotic state. Thus, it is challenging to distinguish faults from unexpected sea current disturbances, which makes these methods prone to resulting in unsatisfactory estimates. (2) The majority of fault detection methods require specific robotics models, which need a complete set of data for model fitting. For instance, in the analytical modeling approach an exact system model is needed for the process, involving data such as observer design, Kalman filter gain update, and particle filter state transition probability distribution. However, it is impractical to obtain a precise mathematical model of the underwater circumstance. In data-driven approach-based fault detection methods, a large amount of data is required for model training, in order to satisfy underwater application requirements. Unfortunately, it is challenging to obtain data in relation to robotics faults, especially in the initial stage of prototype design. (3) The model parameters for state-of-the-art fault detection methods remain constant and lack online adjustment.