Fault Detection Methodology for a Fan Matrix Based on SVM Classification of Acoustic Images (original) (raw)

Applied Condition Monitoring, 2019

Abstract

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.

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