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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Acknowledgements
This work is partially supported by the laboratory of applied precision mechanics (LMPA), Ferhat Abbas University, Setif, Algeria. The authors thank Professor K. LOPARO of case western university for providing the data. Also, the authors gratefully acknowledge the reviewers for their valuable comments and valuable suggestions, which greatly contributed to the improved presentation of this work.
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Sedira, M., Ziani, R., Felkaoui, A. (2019). Comparison Between Hidden Markov Models and Artificial Neural Networks in the Classification of Bearing Defects. In: Felkaoui, A., Chaari, F., Haddar, M. (eds) Rotating Machinery and Signal Processing. SIGPROMD’2017 2017. Applied Condition Monitoring, vol 12. Springer, Cham. https://doi.org/10.1007/978-3-319-96181-1_6
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