Grey relation analysis and prediction of lube oil consumption and crankcase blow-by in piston ring pack for agricultural diesel engine

The piston ring pack is one of the most important components to affect the power, economy and emission of a diesel engine. Among them, lube oil consumption (LOC) and crankcase blow-by (CB) are the main indicators to evaluate the lubrication and sealing performance of the piston ring pack. In this study, the multi-factor analysis was implemented to predict the influencing factors on the LOC, CB and the sensitivity between them in the piston ring pack for an agricultural diesel engine. A YNF40 diesel engine was taken as the research object. Firstly, the test bench of LOC and CB was built for the diesel engine. The kinematics and dynamics models were established using hydrodynamic lubrication and gas flow theoretical of piston pack friction subsets, technical and structural parameters. A series of experimental tests were also carried out to verify the accuracy and reliability of the simulation. Secondly, the grey relational grade (GRG) of different influencing factors was obtained by grey relation analysis (GRA). The sensitivity analysis between the influencing factors and LOC-CB was then carried out using GRG calculation and ranking. Finally, the time-series prediction models were established for the transient variations of LOC and CB under different speed conditions using a back propagation (BP) neural network. The multi-factor GRG analysis indicated that the most sensitive influencing factor on the LOC and CB in the piston ring pack for a diesel engine was the chamfer of the bottom edge of the 1st ring groove of the piston, whereas, the most insensitive influencing factor was the back clearance of the 3rd ring groove of piston. The maximum GRG between LOC and CB and the chamfer of the bottom edge of the 1st ring groove of the piston was 0.89279, whereas, the minimum GRG between LOC and CB and the back clearance of the 3rd ring groove of the piston was 0.58361. The descending order of sensitivity was obtained between the target and influencing factors. The LOC and CB gradually increased in the piston ring pack for a diesel engine, as the chamfer of the bottom edge of the 1st ring groove of the piston increased. The back clearance of the 3rd ring groove of the piston shared no significant effect. The average standard deviation of the variation of LOC and CB with the chamfer of the bottom edge of the 1st ring groove of the piston were 2.81, and 3.90, respectively. By contrast, the average standard deviation of the variation of LOC and CB with the back clearance of the 3rd ring groove of the piston were 0.003 and 0.209, respectively. Therefore, the larger the GRG between LOC and CB, and each influencing factor were, the larger the mean standard deviation of LOC and CB with the influencing factors were, and the more sensitive the objectives were to the influencing factors. The time-series prediction showed that there was a better consistent trend of the predicted and actual values on the transient variations of LOC and CB. The prediction accuracy and reliability of the time series prediction model were higher than before, which were distributed within the 95% confidence band. The research findings can provide effective guidance to improve the thermal efficiency with the less carbon emissions of agricultural diesel engines.