Design Of Fuel Consumption Monitoring System For Ship Based On Internet Of Things

The huge fuel consumption of ships not only affects the environmental protection but also increases the economic costs.Monitoring the ship's fuel consumption has always been a consuming task.With the rapid development of technologies such as Internet of things,big data,and machine intelligence,the traditional method of manually calculating or only picking up several fuel consumption parameters is poor to meet the requirements intelligence,real-time,and refined management now.However,the advantages of the Io T(Internet of Things)has lightweight,short-range,low-cost,networking,used by the ship's monitored equipment that is flexibly equipped with Io T module to collect numbers parameters such as wind,waves,speed,etc.,is a promising practice.After data reduction,calculated,analyzed and compared by programming algorithm,these parameters are sent to the shore station.This method not only obtains the ship's fuel saving consumption and economical best performance,but also guides the ship's automated navigation in turn,further promotes the development of smart ships.Therefore,this paper proposes a ship-based fuel consumption monitoring system based on the Internet of Things,focusing on hardware design of ship terminal,software programming framework,and data mining algorithm.Hardware Modules of Ship Terminal.Sensors,several control modules,and communication modules are designed to monitor ship fuel consumption related equipment.The details include: the installation of sensor modules;the arrangement of ship-borne sensor networks;design of network topology;design of ZigBee,various satellite,wireless WiFi or GPRS/3G/4G/NB-IoT modules etc.integrating to the main control module.Design of Software Framework.The content of software includes software system design of ZigBee module;instruction set design of ZigBee module to Centralized Control Computer;design of data storage structure;compress and reduction for centralized controller;design of software architecture technology based on nodejs + redis;display fuel consumption online monitoring;fuel statistics;fuel historical consumption;fuel abnormal statistics and other interface.Algorithm Design.Since the system focuses on the deviation between normal fuel consumption and fuel estimated consumption,this paper uses the grey correlation degree algorithm to find out the important impact factors of fuel consumption from the mainparameters such as main engine speed,auxiliary,velocity and direction of wind,velocity and direction of ocean current,and to found out the significantly different factors from the normal fuel consumption after the data reduction algorithm.In addition,artificial neural network algorithms are used to predict the theoretical fuel consumption in advance.The practical results show that the entire system is stable and reliable.System characteristics of scalability,reliability,and accuracy has reached the initial goal of design.