Design and Implementation of Digital Twin for Predicting Failures in Automobiles Using Machine Learning Algorithms 2019-28-0159

The drastic technological advancements in the field of autonomous vehicles and connected cars lead to substantial progression in the commercial values of automobile industries. However, these advancements force the Original Equipment Manufacturers (OEMs) to shift from feedback-based reactive business analysis to operational-data based predictive analysis thereby enhancing both the customer satisfaction as well as business opportunities. The operational data is nothing but the parameters obtained from several parts of an automobile during its operation such as, temperature in radiator, viscosity of the engine oil and force applied over the brake disk. These operational data are gathered using several sensors implanted in different parts of an automobile and are continuously transmitted to backend computers to develop Digital Twin, which is a virtual model of the physical automobile. Later, gathered operational data are analyzed using data mining algorithms to predict the failures of an automobile well in advance, better insights into performance of an automobile thereby recommending alternative design choices and remote service management of failures by a professional technician. Firstly, this research work illustrates the platform for the creation of digital twin using Eclipse Hono, Eclipse Kura and Eclipse Ditto. Secondly, it explains about the operational data gathering and processing at the nearby edge devices as well as the remote cloud. Finally, the prediction of failures is demonstrated using Turbofan Engine Degradation Simulation Dataset by means of several machine learning regression algorithms and compare their accuracies. Finally, it is concluded that Gradient Boost Regressor provides better accuracy in predicting future failures.