Fault Detection Methodology for a Fan Matrix Based on SVM Classification of Acoustic Images
A methodology to detect if a fan matrix is working properly has been designed and is presented in this paper. This methodology is based on a Support Vector Machine (SVM) classifier that uses geometrical parameters of the acoustic images of the fan matrix. These acoustic images have been obtained using a 16 × 16 planar array of MEMS microphones working at different frequencies. A fan matrix that is not working properly implies that some of its fans have failed, that is, it does not work. The designed fault detection methodology supposes that these fans fail one by one. If one of the fans is not working, this fact can be detected rapidly with the purposed methodology, and the fan can be repaired or replaced by a new one. Although it is really unusual that more than one fan fails at the same time, this paper also studies how this methodology works if the number of faulty fans increases, in order to know if the methodology is robust enough in the presence of unexpected situations.
KeywordsFault detection Fan matrix Acoustic images SVM
This work is supported by the Spanish research project SAM TEC 2015-68170-R (MINECO/FEDER, UE). The authors also gratefully acknowledge the helpful comments and suggestions of the reviewers, which have improved the presentation.
Conflict of Interest
The authors declare that there is no conflict of interest regarding the publication of this paper.
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