Pattern recognition on diesel engine working conditions by Wavelet Kullback-Leibler distance method

P. Zhou, H. Li, D. Clelland

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

This article introduces a novel pattern recognition and fault diagnosis method for diesel engines. The method is developed from engine vibration signal analysis in combination with wavelet and Kullback-Leibler distance (KLD) approaches. The new approach is termed wavelet Kullback-Leibler distance (WKLD). Experimental data relating to piston and cylinder liner wear obtained from a production diesel engine are used to evaluate the newly developed method. A good agreement between the experimental data and the WKLD estimation is found. The results of this article suggest that WKLD is an advancement on the methods which have been currently developed for pattern recognition and fault diagnosis of diesel engines.
LanguageEnglish
Pages879-887
Number of pages8
JournalProceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science
Volume219
Issue number9
DOIs
Publication statusPublished - 2005

Fingerprint

Pattern recognition
Diesel engines
Failure analysis
Signal analysis
Engine cylinders
Pistons
Wear of materials
Engines

Keywords

  • wavelet
  • fault diagnosis
  • Kullback-Leibler distance
  • pattern recognition
  • diesel engines
  • vibration

Cite this

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title = "Pattern recognition on diesel engine working conditions by Wavelet Kullback-Leibler distance method",
abstract = "This article introduces a novel pattern recognition and fault diagnosis method for diesel engines. The method is developed from engine vibration signal analysis in combination with wavelet and Kullback-Leibler distance (KLD) approaches. The new approach is termed wavelet Kullback-Leibler distance (WKLD). Experimental data relating to piston and cylinder liner wear obtained from a production diesel engine are used to evaluate the newly developed method. A good agreement between the experimental data and the WKLD estimation is found. The results of this article suggest that WKLD is an advancement on the methods which have been currently developed for pattern recognition and fault diagnosis of diesel engines.",
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AU - Li, H.

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AB - This article introduces a novel pattern recognition and fault diagnosis method for diesel engines. The method is developed from engine vibration signal analysis in combination with wavelet and Kullback-Leibler distance (KLD) approaches. The new approach is termed wavelet Kullback-Leibler distance (WKLD). Experimental data relating to piston and cylinder liner wear obtained from a production diesel engine are used to evaluate the newly developed method. A good agreement between the experimental data and the WKLD estimation is found. The results of this article suggest that WKLD is an advancement on the methods which have been currently developed for pattern recognition and fault diagnosis of diesel engines.

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