Causality-based Prediction Method for the Diesel Engine Assembly Line System*

Abstract

The prediction of diesel engine power is a vital prerequisite for diesel engine quality promotion. A key issue of diesel engine power prediction is the selection of representative features for forecasting. However, current feature selection methods mainly rely on correlation analysis which cannot distinguish between direct correlation and indirect correlation. This paper presents a causal feature selection method for diesel engine power forecasting. Causalities distinguish direct influences from indirect ones. Therefore, this paper proposes a diesel engine power prediction framework based on using Markov Blanket-based feature selection approach and Gradient Boosting Decision Tree (GBDT) forecasting model. The proposed framework first applies Markov Blanket to identify causalities between manufacturing variables and diesel engine power and generates a causal feature set. Then, the quantitative relationship between causal features and the diesel engine power is established through GBDT. Finally, the proposed framework is tested by the experiment on a real diesel engine dataset. And the results show that the proposed framework delivers a satisfactory performance advantage for the validation condition in actual applications, the root mean squared error and the coefficient of variation of the root mean squared error of the GBDT model under the validation condition are 2.94kW and 1.17%, respectively.