함정 추진체계 이상진단을 위한 회귀 모델 설계.

The condition-based maintenance system(CBMS) is a technology to improve the survivability and operational performance of naval ships. It monitors the condition of major equipment (propulsion system, power system, etc.) and performs functions such as displaying trends through performance evaluation, generating alarm conditions, fault diagnosis, and predicting remaining life. This paper studies ways to increase the accuracy and efficiency of machine learning model design for abnormal diagnosis of Naval propulsion system equipment. The Random Forest and Support Vector Regression(SVR) models used in overseas cases and the XGBoost and Light GBM models, which are most commonly used in various fields, were used to calculate the normal prediction values(Baseline) based on actual operational naval ship propulsion system equipment, and performed condition diagnosis through comparison with real-time measurements. Then, the performance of each regression model was compared and analyzed through RMSE, R2 Score, and learning time. [ABSTRACT FROM AUTHOR]