# Research on Clustering-Based Fault Diagnosis during ROV Hovering Control
## 5 Conclusions  
This study addresses the FD methodology and the experimental verification for the most frequently occurring external factor-induced faults in ROV thrusters, such as propeller blade damage and entanglement with floating debris. For the diagnostic subject, the small BLDC thrusters, despite being the same model, exhibit performance differences. Therefore, experiments were conducted to verify the differences =in rotational speed performance for each thruster per control input, as well as performance variations due to aging. To verify performance for each control input, the power consumption and RPMs of the underwater thrusters were acquired, and a board was designed to compute RPM by reading the period of the back-EMF for RPM acquisition. Based on the notable performance differences between thrusters, RPM control was implemented to achieve constant thrust across all units by utilizing polynomial curve fitting to define the relationship between rotational speed and control input for each thruster.  
Fault diagnosis techniques are categorized into model-based and data-based methods, each further subdivided into quantitative and qualitative analyses. Given the characteristic performance differences among identical thrusters, the application of model-based fault diagnosis methodologies, which demand precise modeling, is challenging. The FD methodology used in this study was a qualitative data-based method that analyzed the relationship between faults and different unit features, such as RPM and current. A pattern recognition technique, DBSCAN, was applied to distinguish between normal, breakage, and entangled cases based on the selected features. Based on the results of the engineering water tank experiment, it was confirmed that clusters are formed similarly to the expected distribution of data. Future research aims to apply fault-tolerant control strategies for each fault situation to enhance hovering stability.  
Figure 19: Results of applying DBSCAN.  
**Author Contributions:** Conceptualization, H.-S.C. and J.-H.P.; methodology, J.-H.P. and H.C.; software, J.-H.P.; validation, J.-H.P., H.C., S.-M.G., K.-B.C., M.K., J.H., D.J., C.Y. and H.-S.C.; formal analysis, J.-H.P. and H.C.; investigation, [PERSON]-[PERSON] and H.C.; resources, [PERSON]-[PERSON] and [PERSON]; data curation, [PERSON]-H.P.; writing--original draft preparation, [PERSON]-H.P.; writing--review and editing, [PERSON]-H.P., H.C., [PERSON]-M.G., K.-B.C., M.K., J.H., D.J., C.Y. and [PERSON]-[PERSON]; supervision, [PERSON]-S.C.; project administration, [PERSON]-S.C.; funding acquisition, H.-S.C. All authors have read and agreed to the published version of the manuscript.  
This research was supported by Unmanned Vehicles Core Technology Research and Development Program through the National Research Foundation of Korea (NRF) and Unmanned Vehicle Advanced Research Center (UVARC) funded by the Ministry of Science and ICT, the Republic of Korea (NRF-2020M3C1C1A02086321) and this research was supported by Development of mobile laser hull cutting equipment for rapid lifesaving in case of ship capsize (20024457).  
Not applicable.  
The data presented in this study are available in article.  
The authors acknowledge all the members of the Korea Maritime & Ocean University Intelligent Robot & Automation Lab.  
Author [PERSON] was employed by the company LIG NEX1 Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.