Data processing and augmentation of acoustic array signals for fault detection with machine learning

This paper proposes a novel method to detect and localize disturbances in dynamical systems using non-collocated acoustic arrays by processing their data such that established artificial intelligence techniques can be used. Methods are proposed to compress time-domain based separate microphone data into frequency-domain based images. Furthermore, a data augmentation solution is given to significantly reduce the amount of required training data by using augmentation in the time domain. The use of artificial intelligence in the field of condition monitoring and diagnostics is becoming increasingly popular. However, the vast majority of work is based on sensors collocated in the machines. Therefore, the possibility of using acoustic arrays as a non-intrusive contactless sensor is emerging. The goal of this work is to develop data processing methods that allow for analysis of acoustic array images of vibration patterns. These methods have been demonstrated on a newly created experimental dataset where the a vibrating plate is measured using a 32 by 32 microphone array. On this plate, a disturbance mass placed in different positions to modify the dynamic properties of the system. The experimental results show that the proposed methods are able to determine the position of the disturbance mass even with low amounts of training data. They show to be promising for applications where space-frequency information is of essence.