Study on Determination of Excessive Emissions of Heavy Diesel Trucks Based on OBD Data Repaired

Abstract

It has been recognized that emission control for heavy diesel trucks should be given priority, as a massive amount of pollutants (e.g., NOx) are emitted from heavy diesel trucks. Although pollutants can be filtered to a considerable extent by after-treatment devices equipment, emissions can still exceed the designated standards when after-treatment devices function improperly. To timely identify excessive emissions, we propose a general and systematic framework, including a data quality assessment and a data repairing and excessive emission determination process, based on the data sensed from the on-board diagnostics (OBD) monitoring system. To overcome the adverse effects of poor data quality, a set of approaches have been developed for the different statuses of data quality. When all variables contain missing or abnormal values, data repairing algorithms can be employed to improve data quality. Two strategies have been developed for the situation where only the NOx data is problematic. One is to improve data quality by using the other variables before identifying excessive emissions, and the other is to directly predict whether the emissions exceed recommendations by using other variables without the data quality problem. To reduce the impact of noise and extreme values, three methods based on the moving average principle have been developed to generate an aggregated emission level for the determination of excessive emissions. In the experimental study, we employed a number of machine learning algorithms to achieve data repairing and prediction. The support vector machine (SVM) algorithm slightly outperforms the random forests (RF) and gradient boosting decision tree (GBDT) in the prediction of the excessive emission possibility in terms of prediction accuracy. The experimental results indicate that the most accurate data repairing can be achieved by probabilistic principal component analysis (PPCA), as compared to non-negative matrix factorization (NNMF) and k-nearest neighbor (KNN). However, the proposed approach does not restrict other algorithms from achieving the functions of data repairing and prediction.