Trend analysis and early warning of engine service status of corn harvester
In order to improve the operating stability of the corn harvester engine in service,the engine operation data is taken as the analysis object,and a stability operation trend curve of the starter is calculated to judge whether the engine is in a stable state.Firstly,by obtaining operational data from the engine ECU system,the data under normal engine operation is normalized.Then,a prediction model is established based on a BP neural network optimized by genetic algorithm.Predicting the numerical value of oil pressure using four parameters:engine speed,agricultural machinery speed,coolant temperature,and system voltage as inputs,and the final decision coefficient of the prediction model reaches 0.88,which proves that the prediction model has a high degree of fit and can accurately predict the engine oil pressure.Under normal operation of the engine,the predicted deviation of oil pressure is relatively small.The benchmark vector set is constructed by combining the residual of a large number of normal operation oil pressure predicted values and actual operation values with the actual operation values,and the evaluation vector is constructed by combining the residual of 20 000 normal operation oil pressure predicted values and actual operation values with the actual operation values.The distance value between each evaluation vector and the reference vector set is calculated by using Markov distance.This distance value can represent a stability index value under the normal operation state of the engine.The analysis results show that the 20 000 index values obtained have a certain aggregation,and the index value is stable between 0 and 10.Therefore,a trend curve of this index value in the time series can represent a stable trend under the service state of the engine,and can be used to judge whether the engine is in normal or abnormal state.
NETL
NSTL
万方数据
