Engine Misfire Detection with Pervasive Mobile Audio
Author(s)
Siegel, Joshua E
Kumar, Sumeet
Ehrenberg, Isaac Mayer
Sarma, Sanjay E
Date Issued
September 2016
Journal
Machine Learning and Knowledge Discovery in Databases
Publisher
Springer International Publishing
Citation
Siegel, Joshua, et al. “Engine Misfire Detection with Pervasive Mobile Audio.” Machine Learning and Knowledge Discovery in Databases, edited by Bettina Berendt et al., vol. 9853, Springer International Publishing, 2016, pp. 226–41.
Version
Author's final manuscript
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. Keywords: Pervasive sensing, Mobile phones, Sound classification, Audio processing, Fault detection, Machine learning
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1007/978-3-319-46131-1_26
