Publication Type:

Journal Article

Source:

Advances in Intelligent Systems and Computing, Springer Verlag, Volume 515, p.507-514 (2017)

ISBN:

9789811031526

URL:

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85015837993&doi=10.1007%2f978-981-10-3153-3_50&partnerID=40&md5=baed62889c63b756b08126c270b48b58

Keywords:

Artificial intelligence, Automobile engines, Automotive engine, Bigdata, Computation theory, Computational capability, Control systems, Cyclostationary, Driver experience, Ecus (electronic control unit), Engines, Fault detection, Intelligent computing, Learning systems, Pattern classification problems, timing jitter, Vehicle performance, Vibration signal

Abstract:

<p>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.</p>

Notes:

cited By 0; Conference of 5th International Conference on Frontiers in Intelligent Computing Theory and Applications, FICTA 2016 ; Conference Date: 16 September 2016 Through 17 September 2016; Conference Code:189629

Cite this Research Publication

Y. C. Nair, Kumar, S., and Dr. Soman K. P., “Real-time automotive engine fault detection and analysis using bigdata platforms”, Advances in Intelligent Systems and Computing, vol. 515, pp. 507-514, 2017.

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