# Research on Clustering-Based Fault Diagnosis during ROV Hovering Control
## 1 Introduction  
Among the various marine platforms for operations and exploration in the ocean, remotely operated vehicles (ROVs) refer to unmanned submarines remotely controlled via a tether line connected to a vessel/operator [1]. ROVs can perform a range of operations in deep-sea environments that are challenging for people to access because they can be powered continuously and steadily by a tether line or communicate in real-time at high volumes with a surface operator [2, 3]. Owing to these characteristics, ROVs have been utilized for several decades in fields such as marine renewable energy, marine salvage, underwater resource development, marine science research, and inspection, maintenance, and repair of underwater facilities [4, 5]. In particular, they are irreplaceable in the field of vessel and subsea equipment inspection via real-time imaging [6]. Also, work-class ROVs play a leading role in the oil and gas industry because of their stable power supply from ships, large payload capacity, and extensive operational range [6, 7, 8]. The unpredictable underwater environment in which ROVs operate exposes them to corrosion, temperature changes, high pressures, and floating debris, leading to various potential failures. Furthermore, the operation of ROV systems may require tethermanagement systems, launch and recovery systems, and professional operators, requiring more space and ships and increasing the expenses and preparation time [9]. Therefore, considering limited performance, cost, and efficiency, self-inspection for faults during ROV operations is necessary to decide whether to continue or suspend a mission [10]. Underwater thrusters are one of the most crucial components of ROV motion control. Most ROVs utilize them for propulsion whether a trajectory tracking or hovering [11]. Compared with other components, underwater thrusters are more prone to faults because of their direct interactions with the marine environment [12]. Damage to these thrusters can result in difficulties during mission execution [12]. Therefore, fault diagnosis (FD) of underwater thrusters has attracted considerable attention [13]. According to [PERSON], FD typically involves three steps [14]: fault detection, which involves recognizing the occurrence of a problem within the target system for diagnosis; fault isolation, which involves determining the location and type of the fault; and fault identification, which involves determining the magnitude (size) of the fault. [PERSON] et al. divided FD into model- and data-based analyses, which can be quantitative or qualitative [15]. Quantitative analysis involves mathematical formulas between the system's inputs and outputs, whereas qualitative analysis examines the relationships between different unit elements [16]. In most model-based FD applications for marine robotic systems, the models are simplified due to the difficulty of accurately obtaining hydrodynamic coefficients, which affects the FD accuracy [17]. Additionally, the small brushless direct current (BLDC)-type underwater thrusters utilized in this study tend to exhibit performance differences even among identical products. Furthermore, the performance degradation over long-term use has rendered model-based techniques unsuitable. Data-based techniques classify normal and fault states by identifying patterns based on data characteristics using measured parameters, state variables, and residuals as fault features [18]. The objective is to identify faults caused by external factors--particularly entanglement with floating debris and propeller breakage, which are among the most frequently occurring [19]. According to previous FD studies, the consumption current and rotation speed (in revolutions per minute, RPM) of underwater thrusters were selected as fault features for FD under normal conditions, propeller breakage, and entanglement [20]. A qualitative data-based approach was used to analyze the relationships between these different unit elements and faults.