<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>
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
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.