# A Research on Fault Diagnosis of a USV Thruster Based on PCA and Entropy
## 1 Introduction  
Unmanned platforms, such as unmanned surface vehicles (USVs) and unmanned underwater vehicles (UUVs), are actively developed for missions, such as marine environment surveys, monitoring, and management of offshore structures, using underwater thrusters as propulsion devices [1]. Research on improving reliability in autonomous navigation and operational safety has become more important as research on unmanned platforms has advanced [2]; thus, the need for fault diagnosis research has increased. However, unlike fault diagnosis studies for ground vehicles or UUVs [3; 4; 5; 6; 7], there has been a lack of research on fault diagnosis in USVs. In particular, a fault in the underwater thruster may cause critical losses to a mission; therefore, research on fault diagnosis for the underwater thrusters of unmanned platforms is crucial [8]. Moreover, recent studies lack consideration of actual experiments on platforms with thruster faults. It is crucial to conduct fault diagnosis for USV thrusters using actual trials that mimic real-world environments. Previously reported fault diagnosis methods can be categorized into rule-based, model-based and data-driven approaches [6; 9; 10; 11]. The rule-based method is applied to systems with simple structures, and fault diagnosis is performed by setting rules, such as thresholds. The method has been employed in fault diagnosis research based on simulation by setting thresholds for the health level of thrusters and key equipment included in the USV [12]. The rule-based method requires expert knowledge for setting rules, and for each new type of fault additional restrictions need to be fixed, resulting in increased complexity in the system [13; 14]. Moreover, the rule-based analysis method is inadequate in this research, since it is inappropriate to consider possible fault conditions in the future. In the model-based method, a fault is diagnosed through the evaluation of residuals obtained by comparing the estimated values from a model of the entire system or subsystems and the measured values from data acquisition [15]. These model-based techniques have been applied in fault diagnosis research with modeling based on sensor data, such as position, velocity and attitude, and studies on fault-tolerant control [3; 16; 17]. USVs are operated close to the sea surface; therefore, their performance is significantly affected by environmental disturbances, such as wind, waves and currents, to which underwater thrusters are particularly vulnerable. Hydrodynamics induced by these environmental disturbances show highly nonlinear characteristics; hence, modeling such nonlinearity poses a challenge [18]. Therefore, a model-based approach was also considered inadequate for fault diagnosis of a USV thruster. The data-driven approach entails diagnosing a fault by analyzing the pattern of acquired data. This method has no restrictions in terms of having to consider constraints of deriving a system model and has the advantage of being applicable to nonlinear systems [19; 20; 21]. Therefore, in comprehensive consideration of the various factors described above, the data-driven approach was selected as the optimal approach for fault diagnosis of USV thrusters. Examples of the data-driven method include fault diagnosis studies on underwater thrusters using deep convolutional neural networks (DCNNs), one of the deep learning techniques, and t-Distributed Stochastic Neighbor Embedding (t-SNE) algorithms, one of the dimension reduction techniques, using multiple sensor data, such as hydrophone and current sensor data [22]. In addition, there has been research on thruster fault diagnosis based on discrete wave transforms and orthogonal fuzzy neighborhood discriminative analysis, one of the dimensionality reduction methods, using current and vibration data for the diagnosis of breakage conditions of thruster blades [23]. Furthermore, a study was performed using an actual USV to perform a water-tank experiment for the validation of selected fault features of a thruster [24].