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Real-time automotive engine fault detection and analysis using bigdata platforms

Publication Type : Journal Article

Publisher : FICTA

Source : FICTA, Vol 515, pp 507-514, 2016 (Scopus)

Url :

ISBN : 9789811031526

Campus : Coimbatore

School : School of Engineering

Center : Computational Engineering and Networking

Department : Electronics and Communication

Year : 2017

Abstract : This paper is aimed at diagnosing automotive engine fault in real-time utilizing BigData framework called spark. An automobile in the present day world is equipped with millions of sensors which are under the command of a central unit the ECU (Electronic Control Unit). ECU holds all information about the engine. A network of ECUs connected across the globe is a source tap of BigData. Leveraging the new sources of BigData by automotive giants boost vehicle performance, enhance loco driver experience, accelerated product designs. A piezoelectric transducer coupled to the ECU captures the vibration signals from the engine. The engine fault is detected by carving the problem into a pattern classification problem under machine learning after extracting cyclostationary features from the vibration signal. Spark-streaming framework, the most versatile BigData framework available today with immense computational capabilities is employed for engine fault detection and analysis. © Springer Nature Singapore Pte Ltd. 2017.

Cite this Research Publication : Yadhu C Nair, Sachin Kumar S, KP Soman, Real-Time Automotive Engine Fault Detection and Analysis Using BigData Platforms, FICTA, Vol 515, pp 507-514, 2016 (Scopus)

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