Virtual Sensor Modelling using Neural Networks with Coefficient-based Adaptive Weights and Biases Search Algorithm for Diesel Engines

TL;DR

This paper proposes a neural network-based virtual sensor model for diesel engines that employs a novel coefficient-based adaptive weights and biases search algorithm to accurately predict engine performance without physical sensors.

Contribution

It introduces a new adaptive search algorithm for neural network weights and biases to improve virtual sensor modeling in diesel engines.

Findings

Enhanced accuracy of engine performance predictions

Reduced reliance on physical sensors

Cost-effective engine monitoring solution

Abstract

With the explosion in the field of Big Data and introduction of more stringent emission norms every three to five years, automotive companies must not only continue to enhance the fuel economy ratings of their products, but also provide valued services to their customers such as delivering engine performance and health reports at regular intervals. A reasonable solution to both issues is installing a variety of sensors on the engine. Sensor data can be used to develop fuel economy features and will directly indicate engine performance. However, mounting a plethora of sensors is impractical in a very cost-sensitive industry. Thus, virtual sensors can replace physical sensors by reducing cost while capturing essential engine data.