Research on the method of diesel particulate filters carbon load recognition based on deep learning

Because the carbon load inside a diesel particulate filters (DPF) affects the DPF regeneration, and the carbon load recognition is significant for the particulate matter (PM) emission control. It is necessary to investigate an on-board DPF carbon load recognition method because the carbon load cannot be directly measured by sensors. Aiming to build a DPF carbon load prediction model adopting the deep learning method, this paper proposes a DPF carbon load identification model based on different experimental parameters using a layered one dimension convolutional neural network (1D-CNN) method. To improve data validity, this paper adopts two data-processing methods. The data pre-processing adopts data splicing method to complete the construction of the original sample set, and the data after-processing uses wavelet packet transform method to establish the feature sample sets. The model adopts the optimal feature dataset constructed by three input parameters, i.e., temperature difference, pressure difference, and exhaust mass flow, and has both high training accuracy and test accuracy above 90 %. The pressure difference is the most important influencing input parameter, and the three-parameter sample set (ΔT + ΔP + Q) has great recognition accuracy and good model stability with the high training accuracy and test accuracy as well as less iteration. •A heavy-duty diesel engine was tested under various conditions of carbon load inside DPF.•The data processing was done by two methods: data splicing and wavelet packet transform.•We proposed a DPF carbon load identification model based on a one-dimension CNN method.•The model adopts 3 parameters, and the 3-parameter sample set has optimal performance.•The 1D-CNN model has both high training accuracy and test accuracy above 90 %.