Condition monitoring using computational intelligence

Tshilidzi Marwala; Christina Busisiwe Vilakazi

doi:10.4018/978-1-59904-582-5.ch006

Condition monitoring using computational intelligence

Tshilidzi Marwala, Christina Busisiwe Vilakazi

University of the Witwatersrand

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

8 Citations (Scopus)

Abstract

Condition monitoring techniques are described in this chapter. Two aspects of condition monitoring process are considered: (1) feature extraction; and (2) condition classification. Feature extraction methods described and implemented are fractals, kurtosis, and Mel-frequency cepstral coefficients. Classification methods described and implemented are support vector machines (SVM), hidden Markov models (HMM), Gaussian mixture models (GMM), and extension neural networks (ENN). The effectiveness of these features was tested using SVM, HMM, GMM, and ENN on condition monitoring of bearings and are found to give good results.

Original language	English
Title of host publication	Handbook of Computational Intelligence in Manufacturing and Production Management
Publisher	IGI Global
Pages	106-123
Number of pages	18
ISBN (Print)	9781599045825
DOIs	https://doi.org/10.4018/978-1-59904-582-5.ch006
Publication status	Published - 2007
Externally published	Yes

ASJC Scopus subject areas

General Business,Management and Accounting

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10.4018/978-1-59904-582-5.ch006

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BT - Handbook of Computational Intelligence in Manufacturing and Production Management

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ER -

Condition monitoring using computational intelligence

Abstract

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