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Erschienen in:
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2016 | OriginalPaper | Buchkapitel

Engine Misfire Detection with Pervasive Mobile Audio

verfasst von : Joshua Siegel, Sumeet Kumar, Isaac Ehrenberg, Sanjay Sarma

Erschienen in: Machine Learning and Knowledge Discovery in Databases

Verlag: Springer International Publishing

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Abstract

We address the problem of detecting whether an engine is misfiring by using machine learning techniques on transformed audio data collected from a smartphone. We recorded audio samples in an uncontrolled environment and extracted Fourier, Wavelet and Mel-frequency Cepstrum features from normal and abnormal engines. We then implemented Fisher Score and Relief Score based variable ranking to obtain an informative reduced feature set for training and testing classification algorithms. Using this feature set, we were able to obtain a model accuracy of over 99 % using a linear SVM applied to outsample data. This application of machine learning to vehicle subsystem monitoring simplifies traditional engine diagnostics, aiding vehicle owners in the maintenance process and opening up new avenues for pervasive mobile sensing and automotive diagnostics.

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Vorheriges Kapitel Active Learning with Rationales for Identifying Operationally Significant Anomalies in Aviation
Nächstes Kapitel From Plagiarism Detection to Bible Analysis: The Potential of Machine Learning for Grammar-Based Text Analysis
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Metadaten
Titel
Engine Misfire Detection with Pervasive Mobile Audio
verfasst von
Joshua Siegel
Sumeet Kumar
Isaac Ehrenberg
Sanjay Sarma
Copyright-Jahr
2016
Buch
Machine Learning and Knowledge Discovery in Databases

Print ISBN: 978-3-319-46130-4

Electronic ISBN: 978-3-319-46131-1

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-46131-1_26

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