# Online Learning Based Underwater Robotic Thruster Fault Detection
## 7 Conclusions  
In this paper, we presented an online learning based underwater robotics thruster fault detection method, which is able to robustly detect faults in the presence of sea current disturbances and time-varying conditions during the operation of the underwater robot. The main contributions include the following. (1) A fault detection framework based on online learning was proposed, which mainly includes online modeling using a data-driven strategy and modified PSO optimization, with fault detection based on an online updating mechanism. This fault detection frame \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \begin{tabular}{c} **MAE of the Online** \\ **Learning-Based Model** \\ \end{tabular} & \begin{tabular}{c} **MAE of the New-Built Online** \\ **Learning-Based Model** \\ \end{tabular} & \begin{tabular}{c} **MAE of the** \\ **Fixed Model** \\ \end{tabular} \\ \hline \begin{tabular}{c} Sea trial data \\ simulation \\ \end{tabular} & 10.54 & 0.25 & 16.98 \\ \hline \
\begin{tabular}{c} Pool test fault \\ detection \\ \end{tabular} & 1.70 & 0.44 & 5.53 \\ \hline
\begin{tabular}{c} Sea trial fault \\ detection \\ \end{tabular} & 2.91 & 0.24 & 258.23 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: MAE of the propeller speed prediction model. Figure 20: Model residuals of the fixed model-based fault detection method during sea trial of the portable AUV. work has impressive generalizability and is suitable for fault detection based on other state parameters, such as the current. (2) A multi-center particle swarm optimization algorithm with memory ability was proposed, in order to solve the online optimization problem in the process of modeling. We designed an initial optimization strategy based on prior knowledge, historical optimal solution and improved tent mapping, as well as a multi-center collaborative search strategy, which can effectively reduce the odds of becoming stuck in local minima. The possible future improvements of the proposed online learning-based fault detection method are as follows. (1) To facilitate implementation, the proposed method uses polynomial fitting as the basis for establishing the control variable-speed model. However, polynomial fitting is not necessarily the most suitable method in this application. Some other mathematical modeling [44] or machine learning-based [45] methods that can achieve small sample learning [46] or adaptive online learning [47] can be tried. The literature [48] proposes a promising optimal learning method based on deterministic artificial intelligence, which is worthy of further study. (2) In the implementation of the proposed method, residual threshold needs to be determined in advance based on prior knowledge. If the residual error threshold can be automatically generated online during the fault detection process, the adaptiveness of the fault detection algorithm could be further improved. In previous research [49], we proposed an interval prediction method of oscillating time series based on grey system modelling, which may provide some ideas for the estimation of residual threshold. Data curation, Y.Z. ([PERSON]); Investigation, X.L. and G.X. ([PERSON]); Methodology, Y.Z. ([PERSON]); Project administration, G.X. ([PERSON]) and W.G. ; Resources, G.L. ; Software, Y.Z. ([PERSON]). All authors have read and agreed to the published version of the manuscript. This research was funded by Hainan Provincial Natural Science Foundation of China, grant number 520 QN298; National Natural Science Foundation of China, grant number 61903357; Liaoning Provincial Natural Science Foundation of China, grant number 2021 JH6/10500114, 2020-MS-032; China Postdoctoral Science Foundation, grant number 2020M672600; The National Key R&D Program of China, grant number 2020 YFC1521704. Not applicable. Data Availability StatementData are available from the corresponding author, [PERSON], upon request. The authors declare no conflict of interest.