Off-Highway Machine Fuel Performance Prediction Through Engine Data Analytics 2021-26-0319

The field performance of a machine is conventionally analyzed using tools of virtual validation such as physics-based simulation models. Machine performance test data is typically not incorporated for performance evaluation using these tools. The present work aims to demonstrate the use of Data Analytics (DA) as a tool to analyze this data for predictive purposes. It aims at establishing numerical relationships of engineering interest within the data, which would otherwise be complex if done only using physics-based models.

Engine operation data spanning over three months, comprising of multiple channels, of an off-highway machine, is used for model development. Machine fuel burn rate is chosen as the dependent variable. Several independent variables such as engine speed, charge air pressure, NO_x production level, are chosen based on their correlation with the dependent variable and upon engineering interest. Linear regression models are developed which show a good fit and correlation. The model demonstrates a high R^2 value implying high robustness in the choice of predictors. The model established from the training data set is compared with predictions obtained from the validation data set, in an attempt towards model validation. The model so developed is deployed to predict fuel performance, given a set of machine operating parameters. As follow-up steps, alternative algorithms could be explored as well as several techniques of machine learning could be tried to involve more features or further improve prediction accuracy.