Research On Thermal Parameter Monitoring And Diagnosis Technique For Marine Intelligent Diesel Engine

Marine intelligent diesel engine is the power supply of a ship, and its healthful running is the guarantee to the safe voyage of the ship. Thermal parameter monitoring and diagnosis technique for marine intelligent diesel engine was systematically researched, and author of the paper have been able to obtain conclusions as follows.Firstly, the technical characteristic of the intelligent marine engine, and the faults mechanism are discussed in the paper, the program for marine intelligent diesel engine monitoring and diagnosis technique is made sure.Secondly, the system of the diesel engine is divided into several subsystems, such as inlet tube, cylinder, exchange tube, turbo charger and so on. All these subsystems are zero-dimension system, and an universal zero-dimension fault simulation model of the diesel engine is built. Different engine malfunctions under different grades are simulated on the BOOST software. The result of simulation indicates the inner relations between engine the malfunctions and the abnormal thermal parameters, which provides useful reference for fault diagnosis.Thirdly, the feasibility of applying gray relation degree method to diesel engine fault diagnosis is discussed in the thesis, and an improved gray relation algorithm for the diesel engine diagnosis is presented. For the faults in a multi-cylinder engine, the standard states ware obtained from the fault simulation of the diesel engine, which can be used to analyze and to evaluate the cylinder working sates, that is, how close the working states with standard states.Fourthly, this paper design a gray relation degree diagnose system framework, thermal parameter monitoring and diagnosis of the marine intelligent diesel engine is achieved. The system based on CAN fieldbus and data acquisition equipment, were developed by LabVIEW software.Fifthly, due to the complexity of a diesel engine, one monitoring or diagnosing method is sometime difficult to get an accurate result. The demand for multi-sensor information fusion in fault detection, and integration of diagnostic method is raised, for further increasing the accuracy and practicability of the system.