# Online Learning Based Underwater Robotic Thruster Fault Detection
## 6 Experimental Results and Analysis
### Pool Test Fault Detection Results and Analysis  
After the Sea trial data simulation, the proposed online learning-based thruster fault detection algorithm was verified in a pool test, considering the fact that a manual thruster fault is risky and sea trial faults are challenging to capture. A saucer-shape AUV is used for the pool test, as shown in Figure 11, specifications of the saucer-shape AUV and its thruster studied in the pool test is described in Table 2.  
In the pool test, the motion state change was simulated by randomly dragging the saucer-shape AUV. The influence of environmental factors such as ocean currents was simulated by water injection and the formation of a turbulent flow by reflecting the pro \begin{table}
\begin{tabular}{c c c} \hline \hline
**No.** & **Specifications** & **Value** \\ \hline
1 & Main dimensions of the AUV & \(1.6*1.2*0.6\) m \\
2 & Weight of the AUV & 438 kg \\
3 & Thruster control variable type & Voltage \\
4 & Thruster control variable range & \(-5\) V\(\rightarrow\)5 V \\
5 & Maximum propeller speed & 1200 r/min \\
6 & Propeller speed sensor & Integrated Hall sensor \\
7 & Feedback signal of propeller speed sensor & Voltage pulse \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Specifications of the saucer-shape AUV. Figure 11: The saucer-shape AUV in pool test. peller wake on the pool wall. Furthermore, we threw cloth strips into the pool, in order to simulate the thruster intaking unexpected marine debris such as fishing nets or seaweed. Thruster control variable and propeller speed during pool test of the saucer-shape AUV is shown in Figure 12, the model residual data obtained by the proposed fault detection method is shown in Figure 13. In the tests, the model residual threshold was set to \(|e_{t}|\leq 8\) and the modeling range was set as \(w_{\text{min}}=50\), \(w_{\text{max}}=500\). Initially, the thruster system worked in a fault-free state. Near the 1280 th sampling period, the thruster inhaled the first cloth strips and the thruster is malfunctioning. Near the 1380 th sampling period, the thruster inhaled a second cloth strips, and another failure state occurred. Thruster of the saucer-shape AUV entangled with cloth strips is shown in Figure 14.  
As can be seen in Figure 13, the residual exceeded the threshold in the 698 th and 1096 th sampling periods. After model updating online, the residual of the new-built model had fallen within the threshold, and the system triggered no false alarm for fault detection. There were obvious trends in the variation of model residuals in the 500-700 sampling periods and 700-900 sampling periods. Combined with the change of control variable and propeller speed in the test process, the model residual change was mainly caused by a change of thruster operation status. In the previous analysis, we mentioned that the gradual changes in the working environment and the system status will lead to a change in the system model. The phenomenon was observed in the experiment, consistent with the analysis, which demonstrates that it is reasonable to use a data-driven approach to build and update the system model online. Figure 12: Thruster control variable and propeller speed during pool test of the saucer-shape AUV. As mentioned before, near the 1280 th sampling period, the thruster inhaled the first cloth strips and became entangled. It can be seen from Figure 8 that the model residual exceeded the threshold in the 1282 nd sampling period. At that time, the fault detection algorithm started the online model update mechanism, however, the residual of the new  
Figure 14: Thruster of the saucer-shape AUV entangled with cloth strips in the pool test. Figure 13: Model residuals of the online learning-based fault detection method during pool test of the saucer-shape AUV. built model still exceeded the threshold. Therefore, the algorithm updated the model online for the second time after collecting a new set of work status data in the 1283 rd sampling period.