NDLI: Fourier transform and correlation-based feature selection for fault detection of automobile engines

Content Provider	IEEE Xplore Digital Library
Author	Ghaderi, H. Kabiri, P.
Copyright Year	2012
Description	Author affiliation: School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran (Ghaderi, H.; Kabiri, P.)
Abstract	Recently, research on effective Acoustic Emission (AE)-based methods for condition monitoring and fault detection has attracted many researchers. Due to the complex properties of acoustic signals, effective features for fault detection cannot be easily extracted from the raw acoustic signals. To extract representative features, signal processing techniques play an important role. One of the commonest techniques is Fast Fourier Transform (FFT). This method depends on the variations in frequency domain to distinguish different operating conditions of a machine. In this study, the intension is to categorize the acoustic signals into healthy and faulty classes. Acoustic emission signals are generated from four different automobile engines in both healthy and faulty conditions. The investigated fault is within the ignition system of the engines while they might suffer from other possible problems as well that may affect the generated acoustic signals. The energy of FFT coefficients of acoustic signals for different frequency bands are calculated as features. Correlation-based Feature Selection (CFS) algorithm is used to reduce the dimensionality of the dataset. The case study is carried-out on 4 different types of automobiles using 480 automobiles to prove the independency of the proposed approach on the type of the automobile. Classification results are reported to be around 88 percent accuracy.
Starting Page	514
Ending Page	519
File Size	760176
Page Count	6
File Format	PDF
ISBN	9781467314787
e-ISBN	9781467314794
DOI	10.1109/AISP.2012.6313801
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-05-02
Publisher Place	Iran
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Condition monitoring Correlation-based Feature Selection (CFS) Fault detection Fast Fourier Transform (FFT) Feature extraction Acoustics Automobiles Acoustic Emission (AE) Engines Signal resolution
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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