Spark Plug Fault Recognition Based on Sensor Fusion and Classifier Combination using Dempster–Shafer Evidence Theory

A proper intelligent approach was developed for fault diagnosis of spark plug in an IC engine based on acoustic and vibration signals using sensor fusion and classifier combination. Wavelet de-nosing technique was used for removing the signal noises. ANN and LS-SVM were employed in classification st...

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Main Authors:	R., Mamat, Moosavian, Ashkan, Khazaee, Meghdad, G., Najafi, Kettner, Maurice
Format:	Article
Language:	English
Published:	Elsevier Ltd 2015
Subjects:	TJ Mechanical engineering and machinery
Online Access:	http://umpir.ump.edu.my/id/eprint/8803/ http://umpir.ump.edu.my/id/eprint/8803/ http://umpir.ump.edu.my/id/eprint/8803/ http://umpir.ump.edu.my/id/eprint/8803/1/fkm-2015-rizalman-Spark%20Plug%20Fault.pdf

id	ump-8803
recordtype	eprints
spelling	ump-88032018-01-30T07:18:29Z http://umpir.ump.edu.my/id/eprint/8803/ Spark Plug Fault Recognition Based on Sensor Fusion and Classifier Combination using Dempster–Shafer Evidence Theory R., Mamat Moosavian, Ashkan Khazaee, Meghdad G., Najafi Kettner, Maurice TJ Mechanical engineering and machinery A proper intelligent approach was developed for fault diagnosis of spark plug in an IC engine based on acoustic and vibration signals using sensor fusion and classifier combination. Wavelet de-nosing technique was used for removing the signal noises. ANN and LS-SVM were employed in classification stage. D–S evidence theory was applied to increase the fault detection accuracy. The results showed that the classification accuracies of ANN were 67.46% and 65.08% based on the acoustic and vibration signals. For LS-SVM, the classification accuracies of 65.08% and 57.94% were achieved based on the acoustic and vibration signals. By employing D–S theory, the classification accuracy reached a high level of 98.56%. The results indicated that the data fusion method improved significantly the performance of the intelligent approach in spark plug fault detection. The simultaneous use of acoustic and vibration signals increased the effectiveness of diagnostic system in engine condition monitoring. Moreover, the results demonstrated that the proposed procedure had great potential in spark plug fault recognition. Elsevier Ltd 2015 Article PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/8803/1/fkm-2015-rizalman-Spark%20Plug%20Fault.pdf R., Mamat and Moosavian, Ashkan and Khazaee, Meghdad and G., Najafi and Kettner, Maurice (2015) Spark Plug Fault Recognition Based on Sensor Fusion and Classifier Combination using Dempster–Shafer Evidence Theory. Applied Acoustics, 93. pp. 120-129. ISSN 0003-682X http://dx.doi.org/10.1016/j.apacoust.2015.01.008 DOI: 10.1016/j.apacoust.2015.01.008
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	TJ Mechanical engineering and machinery
spellingShingle	TJ Mechanical engineering and machinery R., Mamat Moosavian, Ashkan Khazaee, Meghdad G., Najafi Kettner, Maurice Spark Plug Fault Recognition Based on Sensor Fusion and Classifier Combination using Dempster–Shafer Evidence Theory
description	A proper intelligent approach was developed for fault diagnosis of spark plug in an IC engine based on acoustic and vibration signals using sensor fusion and classifier combination. Wavelet de-nosing technique was used for removing the signal noises. ANN and LS-SVM were employed in classification stage. D–S evidence theory was applied to increase the fault detection accuracy. The results showed that the classification accuracies of ANN were 67.46% and 65.08% based on the acoustic and vibration signals. For LS-SVM, the classification accuracies of 65.08% and 57.94% were achieved based on the acoustic and vibration signals. By employing D–S theory, the classification accuracy reached a high level of 98.56%. The results indicated that the data fusion method improved significantly the performance of the intelligent approach in spark plug fault detection. The simultaneous use of acoustic and vibration signals increased the effectiveness of diagnostic system in engine condition monitoring. Moreover, the results demonstrated that the proposed procedure had great potential in spark plug fault recognition.
format	Article
author	R., Mamat Moosavian, Ashkan Khazaee, Meghdad G., Najafi Kettner, Maurice
author_facet	R., Mamat Moosavian, Ashkan Khazaee, Meghdad G., Najafi Kettner, Maurice
author_sort	R., Mamat
title	Spark Plug Fault Recognition Based on Sensor Fusion and Classifier Combination using Dempster–Shafer Evidence Theory
title_short	Spark Plug Fault Recognition Based on Sensor Fusion and Classifier Combination using Dempster–Shafer Evidence Theory
title_full	Spark Plug Fault Recognition Based on Sensor Fusion and Classifier Combination using Dempster–Shafer Evidence Theory
title_fullStr	Spark Plug Fault Recognition Based on Sensor Fusion and Classifier Combination using Dempster–Shafer Evidence Theory
title_full_unstemmed	Spark Plug Fault Recognition Based on Sensor Fusion and Classifier Combination using Dempster–Shafer Evidence Theory
title_sort	spark plug fault recognition based on sensor fusion and classifier combination using dempster–shafer evidence theory
publisher	Elsevier Ltd
publishDate	2015
url	http://umpir.ump.edu.my/id/eprint/8803/ http://umpir.ump.edu.my/id/eprint/8803/ http://umpir.ump.edu.my/id/eprint/8803/ http://umpir.ump.edu.my/id/eprint/8803/1/fkm-2015-rizalman-Spark%20Plug%20Fault.pdf
first_indexed	2023-09-18T22:06:46Z
last_indexed	2023-09-18T22:06:46Z
_version_	1777414760694808576

Spark Plug Fault Recognition Based on Sensor Fusion and Classifier Combination using Dempster–Shafer Evidence Theory

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