# A Research on Fault Diagnosis of a USV Thruster Based on PCA and Entropy
## 6 Conclusions  
In this study, a new fault diagnosis technique which considers correlations between the features of fault conditions of blade breakage and entanglement with floating objects was proposed and analyzed. The proposed fault diagnosis algorithm was developed using PCA and entropy methods. The PCA technique was applied for the visualization of data with reduced dimensionality while preserving the structure and characteristics of the data. To accelerate the diagnostic process and make it more convenient, entropy was used. To diagnose the selected faults, actual data were obtained using a USV with the underwater thruster attached. The thruster's vibration, current, RPM and input voltage data were chosen as fault features based on a series of formulas and inferences. Through a number of experiments, the viability of the chosen features was verified, and the vibration and current were observed to be more fault-sensitive than other features. Fault detection was performed using the results and entropy values in Tables 2 and 3. The entropy value in the normal condition was pre-determined as 1, and the faults were detected by comparing it with the entropy value in each fault condition. In this study, the thresholds were temporarily defined, such that the values could be changed by obtaining additional datasets. Entropy values were also used to categorize the faults and evaluate whether the data changes were large (thick-rope and net entanglements) or small (other conditions). The vibration and current change were significant with relatively large changes in data. Based on this analysis, the scaling parameter in this study was adjusted to reduce the impact of RPM. Therefore, vibration and current were located in PC 1 and PC 2. On the other hand, when the data change was relatively minimal, the adjusted scaling parameter increased the influence of RPM. Hence, RPM could be found in PC 1 in numerous instances. Nevertheless, in order to concentrate on the analysis of current and vibration, the data were projected on a plane of these axes. On this plane, the data distribution was diagnosed to classify other conditions (breakages and thin-rope entanglement). Through this proposed fault diagnosis algorithm, it was possible to classify the fault conditions of thruster blade breakage and entanglement of floating objects. Furthermore, the possibility of fault diagnosis based on fault severity was also confirmed, thus verifying the performance of the proposed algorithm. As analyzed using the actual USV fault experimental results in Section 3.3, the values for each fault feature were inconsistent among trials. It was assumed that the causes were nonlinearity of the USV, environmental disturbance and sensor noise. The correlation between fault features was examined to overcome this limitation, and the results of multiple data were visualized for accessible analysis. In addition, for a more intuitive classification, the faults were diagnosed by checking the types of features influenced by each principal component axis and the data distributions. Unfortunately, in this study, the fault diagnosis of breakage and entanglement for a USV thruster with visual examination required a human's subjective evaluation. This resulted from the different fault conditions being categorized using the three axes of 3D space. Nevertheless, the findings of this study show the potential for the diagnosis of the severity of faults as well as the types of faults. More intuitive classifications can be anticipated if the number of cases that must be categorized can be decreased. In addition, if the scaling parameters can be properly identified during data preprocessing, this algorithm can be applied to other systems, as well as in environments significantly affected by disturbances. Furthermore, although automatic fault detection was demonstrated by quantification using entropy, human evaluation was still required for fault diagnosis. Therefore, a follow-up study on fully automatic fault diagnosis is needed. Conceptualization, H.-S.C. and K.-B.C.