Ocean economy plays a crucial role in the strengthening maritime safety industry and in the welfare of human beings. Electric Submersible Pumps (ESP) have been widely used in floating platforms on the sea to provide oil for machines. However, the ESP fault may lead to ocean environment pollution, on the other hand, a timely fault diagnosis of ESP can improve the ocean economy. In order to meet the strict regulations of the ocean economy and environmental protection, the fault diagnosis of ESP system has become more and more popular in many countries. The vibration mechanical models of typical faults have been able to successfully diagnose the faults of ESP. And different types of sensors are used to monitor the vibration signal for the signal analysis and fault diagnosis in the ESP system. Meanwhile, physical sensors would increase the fault diagnosis challenge. Nowadays, the method of neural network for the fault diagnosis of ESP has been applied widely, which can diagnose the fault of an electric pump accurately based on the large database. To reduce the number of sensors and to avoid the large database, in this paper, algorithms are designed based on feature extraction to diagnose the fault of the ESP system. Simulation results show that the algorithms can achieve the prospective objectives superbly.

본 연구는 다양한 기상변화에 따른 안전을 확보하기 위한 해상에서의 플로팅 플랫폼의 연결에 대하여 연구하였다. 본 연구의 목적은 다양한 해상변화가 일어나는 연안역에 설치되는 플로팅 구조물에 관한 문헌 조사를 실시한 후, 콘크리트 구조물 연결하는 모델을 제시하는 것이다. 본 연구에서는 플로팅 구조의 개념, 기존의 모델, 설계적 평가를 포함한 콘크리트 구조물 기반 플로팅 플랫폼을 기술하였고, Rigid Pontoon Connector를 국내 연안역에 적합한 모델로 제안하였다. 연구의 결과로서, 리기드 폰툰 커넥터 디자인은 (Rigid Pontoon Connector) 안정적으로 플로팅 플랫폼의 연결을 구축할 수 있는 매우 효율적이고 실용적인 솔루션으로 분석되었다.