# Research on Clustering-Based Fault Diagnosis during ROV Hovering Control
## 1 Introduction
In this study, the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) pattern recognition method was employed as the FD methodology. This method identifies clusters on the basis of the inclusion of the minimum number of points (MinPts) within an effective range (\(\mathcal{E}\)) centered around a point. DBSCAN has several advantages. (1) Because the distribution of data in space is clustered according to density, there is no need to specify the number of clusters separately [21]. (2) It increases the clustering accuracy by excluding noise, which can be caused by sensor measurement errors and environmental disturbances, such as waves and currents in marine environments. (3) Among clustering techniques, it has the advantages of short computation times and excellent performance in outlier detection [22]. Because of these advantages, DBSCAN is widely used for fault detection and FD [23; 24; 25; 26]. [PERSON] et al. applied DBSCAN to diagnose the potential thermal runaway in batteries installed in electric vehicles [27]. [PERSON] et al. used DBSCAN to increase the accuracy of FD in fuel cells by removing outliers from pressure-sensor data [28]. Considering these advantages and related research, the objective of this study was to diagnose faults for the propeller breakage and entanglement of underwater thrusters by applying DBSCAN. This study focuses on the fault diagnosis of underwater thrusters during the hovering operation of ROVs. ROVs perform specialized tasks such as sample collection, submarine cable and pipeline installation, and real-time video inspections, all of which rely on hovering [29; 30; 31]. Hovering is essential for overcoming the effects of the marine environment, particularly currents, and achieving attitude stabilization and depth control in ROVs. It provides the fundamental functionality necessary for mission continuity and stability. However, while many studies have diagnosed faults in thrusters fixed to structures, research on fault diagnosis during actual hovering operations of ROVs was scarce, and experimental verification was inadequate. Therefore, this study aims to diagnose faults occurring during the hovering operation of ROVs. Moreover, even for two thrusters of the same type, performance differences can occur, and long-term use can lead to performance degradation, causing variations in the rotation speed for the same control input. To address these issues, RPM control was performed to equalize the rotation speeds of underwater thrusters with different performances according to their control inputs. The RPM was measured using the back electromotive force (back-EMF) of a sensorless BLDC motor, and polynomial curve fitting was used to approximate the RPM curve for each control input. The most common external factors that can lead to underwater thruster faults, including propeller breakage and entanglement with floating debris, were imitated. During hovering operations of an ROV in an engineering water tank, current consumption and RPM data were collected for different fault imitation cases. DBSCAN--a pattern recognition technique--was applied to the acquired data. The proposed FD method's performance was verified through experiments in an engineering water tank. The remainder of this paper is organized as follows. Section 2 presents the types of underwater thruster faults in this study and the fault diagnosis and hovering methodologies applied. Section 3 describes the ROV's hardware (HW) and software (SW) systems constructed to validate the proposed FD methods. Section 4 covers the experimental process in the engineering water tank, results, and application outcomes of the FD. Finally, Section 5 concludes the paper.