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.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Design and Implementation of Digital Twin for Predicting Failures in Automobiles Using Machine Learning Algorithms


    Additional title:

    Sae Technical Papers



    Conference:

    International Conference on Advances in Design, Materials, Manufacturing and Surface Engineering for Mobility ; 2019



    Publication date :

    2019-10-11




    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English




    Predicting Human Thermal Comfort in Automobiles

    Bharathan, Desikan / Rugh, John P. | SAE Technical Papers | 2005


    Predicting Human Thermal Comfort in Automobiles

    J. Rugh / D. Bharathan / L. Chaney | NTIS | 2005


    Predicting human thermal comfort in automobiles

    Rugh,J.P. / Bharathan,D. / National Renewable Energy Lab.,US | Automotive engineering | 2005


    PREDICTION OF FAILURES OF VEHICLE BASED ON DIGITAL TWIN SIMULATION

    MASUDA YUKIHITO / KIM BAEKGYU / SHIRAISHI SHINICHI | European Patent Office | 2019

    Free access

    Towards a Machine Learning Algorithm for Predicting Truck Compressor Failures Using Logged Vehicle Data

    Nowaczyk, Sławomir / Prytz, Rune / Rögnvaldsson, Thorsteinn et al. | BASE | 2013

    Free access