Seafaring Business and Fault Evaluation of a Floating Platform- Applied Electric Submersible Pump

Abstract

The growth of the marine safety sector and the general well-being of humanity are directly tied to the health of the ocean economy. In order to keep the machinery on offshore platforms running, electric submersible pumps (ESP) are often employed. While ESP failure may lead to contamination in the water, identifying the problem early can boost the marine economy. Fault detection of ESP systems is becoming more common as nations strive to conform to ocean economics and environmental protection rules. ESP issues have been effectively diagnosed using vibration mechanical models of common failures. The vibration signal is monitored by several sensors in order to perform signal analysis and problem diagnostics in the ESP system. Meanwhile, the difficulty of defect identification would grow if physical sensors were used. In recent years, neural network techniques have been extensively used for ESP fault detection because of their ability to effectively identify electric pump faults using a huge database. In this research, methods are developed based on feature extraction to detect the malfunction of the ESP system, which reduces the number of sensors required and eliminates the need for a huge database. The simulation results demonstrate that the algorithms perform well in meeting the envisioned goals.