Development of Fault Detector with Acoustic Emission Discrimination for Mechanical Motors

Abstract

The autonomous fault diagnosis of mechanical systems is crucial to addressing smart manufacturing product issues. In this article, we propose intelligent diagnosis and prediction technologies based on acoustic emission (AE) for mechanical motors. The integration of practical technologies, such as acoustic analysis, artificial intelligence (AI), edge computing (EC), electromagnetics, communication, and other theory-based subjects, is convenient for achieving flexible changes made in response to the edge operation trend. The proposed model, developed using acoustic information links with machine learning (ML) platforms to collect acoustic information via feature extraction (FE), is novel in that it can detect system health and prevent system failures. It can inspire innovative design concepts once the above model is combined with the EC migration module. In addition, in this paper, we discuss the embedded system in smart manufacturing applications, including AE, to establish an ML framework that is trained using audio emission data. The valuable results from the proposed algorithm experiments show that the audio judgment accuracy rate can be above 90%. At the current stage, the metric accuracy and precision of mechanical motor discrimination can reach 93.5% and 0.97, respectively. In this paper, we present an analytical method for performing motor axis misalignment judgment based on tiny machine learning (TinyML) techniques, which will enable the IoT field to move toward smart energy savings.