Fault Detection in Rotating Machinery Based on Sound Signal Using Edge Machine Learning

Fault detection at the early stage is very important in modern industrial processes to avoid failure with life-threatening results and to reduce the cost of maintenance and machine downtime. In this paper, we present a workflow for building a fault diagnosis system based on acoustic emission (AE) using machine learning (ML) techniques. Our fault diagnosis approach is implemented on an embedded device with the internet of things (IoT) connectivity for real-time faults detection and classification in rotating machines. The achieved accuracy for our approach with a fine decision tree ML model is 96.1%.