Research On Performance Degradation And Health Management Method Of Operating Ship's Main Diesel Engine

The concept of smart and ummaned ships has placed new challenges on the reliability,maintenance,safety and life long cost of the ship's system.Prognosis and Health Management,or PHM,which has been successfully adopted in aerospace and military field,is one of the new topics in the research of ship engine maintenance and management.Currently the monitor of ships' engine is mainly based on thermal parameters.Meanwhile,because of the lack of data,prolonged period to report and fault sample shortage,the PHM has some limitations in real ship application such as the state monitoring,diagnose and prognosis.Aiming for the data deficiency of real ship,this dissertation is focused on the degradation modeling of the ship engine and the health management method which based on engine deterioration according to the technical framework of PHM.This dissertation consists of four aspects as follows:(1)The fundamental research on PHM of ship engine.This dissertation designed the layered fusion and took the region class health management as the main research area.Additionally,the author established the algorithm selection process of each technical part of PHM for the ship main engine,therefore focused on the PHM method from the aspect of performance deterioration.Besides,this dissertation suggests the integrated ship database which consist of sensor monitored data,maintenance incident and external meteorology data.Through the comparison analysis of external meteorology data and sensor data,it shows that through timing and spatial interpolation,the external meteorology data can be used in this research.(2)The research of assessment method for the engine performance in operating ship environment.Based on the fact that the sensor data are primarily thermal parameters,the author suggests a nonlinear state space model for the engine according to average model of diesel engine which suitably on identification,and use compressor efficiency,volumetric efficiency,indicated thermal efficiency and turbo efficiency map to describe the engine performance.By fitting the shop test data of the diesel engine,the initial performance map of the main engine is obtained.For the missing sensor data problem,this dissertation used the nonlinear pattern recognition to obtain the the performance shift coefficient and missing data,and determine the criteria function where the derivative of state variable serves as independent variables,which could avoid the linearized and discretized error that results from prolonged period of data.In the end,a validation case for engine performance assessment is performed on an ocean going ship which sails from high latitude to low latitude.The results show that even thought missing data,this assessment method could make a good estimate with error between 0.5%-1.5%.This method can fully describe the diesel engine performance as the maps covers all the working condition.(3)The modeling of engine deterioration.Based on the sensor data of 507 hours of continuous running of a ship engine,we obtain the time series of 4 parameters utilizing the performance assessing method.Through multivariate analysis of covariance,the effect of operating condition to performance is eliminated,we observe that the downtrend of engine performance parameter and the effect of maintenance incident to engine performance,and prove the increment of deterioration of engine is a stable stochastic process that is related to working condition.Based on this,the author established a second order logarithmic function to describe the effect of working condition to the deterioration.At the same time,this dissertation formed a engine variable speed degradation model based on the Wiener process,estimated the parameters of deterioration model combined with Monte Carlo and maximum likelihood estimation,and get the qualitative relation of degradation speed to working condition.The result indicates that compared to linear Wiener process model,variable-speed degradation model is able to predict the deterioration of engine more accurately.(4)Research on the estimated remaining useful life(RUL)and health management method of ship engine.To solve the problem that estimated the RUL which based on the vaiable speed degradation model need the engine working condition as input,this dissertation established a mathematical model that combined the propulsion system-ship motion-environment.This model can make an estimation on the boundary condition of the engine(air temperature,atmosphere pressure and engine power)with the engine speed,route and weather forecast known.With the prediction on the missing data,this model can achieve the entire estimation on working parameter of ship engine.Additionally,this dissertation researches on the optimal maintenance decision method when on port and maintenance period while on sail respectively.The result indicates that the prediction error of voyage is 3.7%,the average error of the main engine power prediction is 4.7%,the average error of the working condition parameters is l.l%-4.8%,the maximum prediction error of the performance parameter degradation is 4.4%-5.9%.By optimizing the turbine cleaning period of the ship during the voyage,the fuel consumption of the voyage is reduced by 5.9t and the degradation of turbine efficiency is reduced by 2.5%,which proves the effectiveness of the method.