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2019 | OriginalPaper | Buchkapitel

Comparison Between Hidden Markov Models and Artificial Neural Networks in the Classification of Bearing Defects

verfasst von : Miloud Sedira, Ridha Ziani, Ahmed Felkaoui

Erschienen in: Rotating Machinery and Signal Processing

Verlag: Springer International Publishing

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Abstract

In this paper a comparative study between two classification methods was presented, the first one belongs to the statistical domain in this case the Hidden Markov Models (HMM), the second is an Artificial Intelligence (AI) tool known as of Artificial Neural Networks (ANN), given their popularity in recent years and the interest shown by researchers in these methods, as to their performance and efficiency in the field of classification mainly. Indeed, the two classification tools were tested on data collected from vibratory signals on a test bench at the Bearing Data Center of Case Western Reserve University, and after being put in the appropriate form by an adequate signal processing and analysis to facilitate implementation. In this study, we have tried to identify the advantages and disadvantages of both tools in the field of classification of rotating machine defects, with the aim of accessing other work for the implementation of a classifier as effective as efficient. The results obtained are described as satisfactory and encouraging by their compatibility with those obtained by others implemented by other research but in other fields such as speech processing or image processing, which will give the character of originality to our work once completed.

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Nächstes Kapitel On-line Adaptive Scaling Parameter in Active Disturbance Rejection Controller

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Titel: Comparison Between Hidden Markov Models and Artificial Neural Networks in the Classification of Bearing Defects
verfasst von: Miloud Sedira
Ridha Ziani
Ahmed Felkaoui
Verlag: Springer International Publishing
Buch: Rotating Machinery and Signal Processing
Print ISBN: 978-3-319-96180-4

Electronic ISBN: 978-3-319-96181-1

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-319-96181-1_6

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