Method of Exhaust Temperature Estimation and Monitoring of Diesel Engine

Looking at the current scenario in transportation industry, in majority of the conventional powertrains, internal combustion (IC) engines fueled by diesel serve as the powerhouse. In all locomotives driven by IC engine, it is essential to monitor critical engine parameters to ensure good engine health and performance. Exhaust temperature of engine is a very critical parameter which gives the information about in-cylinder combustion. In traditional diesel engine layouts, exhaust temperature measurement relies on physical temperature sensor. The proposed methodology is focused on applying the data driven models for providing an estimated value of the exhaust temperature. Based on the estimated value of exhaust temperature, this technique can be used to monitor the IC engine. This methodology uses an advanced Artificial Intelligence technique for providing an accurate estimate of exhaust gas temperature. Real world vehicle data was used for training, validating, and testing the data driven model. The data driven model is python based and incorporates the use of Keras. Keras is an application programming interface (API) used for building neural networks. Owing to complex multilayer architecture of the neural network and deep learning capabilities, this methodology provides accurate estimation of exhaust temperature in different ambient temperatures and different operating conditions of engine. Time series analysis and statistical analysis between actual exhaust temperature and estimated exhaust temperature is done for understanding the estimation capability of the model. Statistical analysis is in terms of mean absolute error (MAE) and R-squared score. Using this estimation, continuous monitoring of exhaust temperature is done. By providing accurate exhaust temperature estimation, proactive maintenance is facilitated by this methodology thereby enhancing reliability and cost-efficiency of diesel engines.