A study on the correlation coefficients of exhaust gas temperature at the main engine cylinder of middle-size container ships for fault detection

Abstract

Recently, smart and autonomous ships have become hot issues at the International Maritime Organization (IMO) and ship building industry. Smart ships will be completed in the near future because the environmental requirements of the IMO, such as the reduction of greenhouse gas, carbon particle, and NoX emissions, have become increasingly severe. The ship building industry has attempted to solve optimal and economic ship operation problems and comply with these requirements with the digitalization of ship operations using information and communication technology. One of the most important functionalities for smart ships is fault detection and diagnosis system for machineries, especially the main engines and dynamo engines. Major machineries in engine rooms are composed of diesel engines, heat exchangers for diesel engine operations, and pumps and motors for the operation of heat exchangers. To detect faults in running machineries, it is necessary to treat data and detect faults in real time. Recently, several methods using artificial intelligence have been suggested, but their implementations onboard have some limitations. In this study, a statistical method utilizing Pearson correlation coefficient (CC) is used to detect faults in running machines without additional sensors. This study analyzes 6 months’ worth of running data of 24 middle-size ships to analyze the CCs of various items in the main diesel engine using IBM SPSS Statistics. The number of used data is over 10,000. The set of ships includes a maiden voyage ship, 10 to 30 voyages, and over 40 voyages to investigate the changes in CCs for each item depending on the age of the ship. The duration of one voyage in this shipping company is almost under 2 weeks. This paper proposes the CC level of each item in the main diesel engine for the detection of faults.