Fan Fault Diagnosis Using Acoustic Emission and Deep Learning Methods

Abstract

The modern conception of industrial production recognizes the increasingly crucial role of maintenance. Currently, maintenance is thought of as a service that aims to maintain the efficiency of equipment and systems while also taking quality, energy efficiency, and safety requirements into consideration. In this study, a new methodology for automating the fan maintenance procedures was developed. An approach based on the recording of the acoustic emission and the failure diagnosis using deep learning was evaluated for the detection of dust deposits on the blades of an axial fan. Two operating conditions have been foreseen: No-Fault, and Fault. In the No-Fault condition, the fan blades are perfectly clean while in the Fault condition, deposits of material have been artificially created. Utilizing a pre-trained network (SqueezeNet) built on the ImageNet dataset, the acquired data were used to build an algorithm based on convolutional neural networks (CNN). The transfer learning applied to the images of the spectrograms extracted from the recordings of the acoustic emission of the fan, in the two operating conditions, returned excellent results (accuracy = 0.95), confirming the excellent performance of the methodology.