Machine health diagnostics using acoustic imaging and algorithms for machine learning

Nowadays monitoring health conditions of machines is necessary to reduce costs and repairing time and to secure the quality of the products. Therefore, the potential of acoustic measurements in combination with machine learning techniques for non-invasive diagnostics of machine performance has been investigated. The idea is to establish relations between the acoustic images produced by a sound camera and the machine conditions and then create a strategy for processing the images using Convolutional Neural Networks. Several working conditions of the machine have been considered and experiments have been performed both under nominal and abnormal conditions of the machine, obtained by mimicking the presence of a disturbance. The use of the algorithms for image classification allows isolation of the faults in the machine behaviour by the definition of the primary sound sources. The procedure shows promising results with a short computational time, easy application and high accuracy.