Characteristic Change Detection of Diesel Engine Airpath System Using Neural Network and Support Vector Machine

Turbochargers and exhaust gas recirculation (EGR) systems are essential devices for improving the combustion efficiency of diesel engines. If these characteristics change, the desired combustion efficiency and exhaust emission performance cannot be achieved. Therefore, there is a need for a mechanism to detect and report changes in characteristics in a timely manner. In this study, we investigated a method for detecting changes in characteristics and identifying the responsible engine device using neural networks (NN) and support vector machines (SVM) from existing sensor information. Under normal operating conditions, where engine characteristics remain unchanged, a NN is developed to estimate various sensor signals and internal signals of the engine control unit (ECU) based on input signals such as throttle valve opening and EGR valve opening. When changes occur in the engine devices, discrepancies arise between the NN's estimated signals and the actual values. To identify the device with altered characteristics, an SVM is constructed based on the estimation errors of the signals. The effectiveness of the proposed method was demonstrated through a simulation using an engine simulator.