IoT System for Detecting the Condition of Rotating Machines Based on Acoustic Signals

Abstract

Modern predictive maintenance techniques have been significantly improved with the development of Industrial Internet of Things solutions which have enabled easier collection and analysis of various data. Artificial intelligence-based algorithms in combination with modular interconnected architecture of sensors, devices and servers, have resulted in the development of intelligent maintenance systems which outperform most traditional machine maintenance approaches. In this paper, a novel acoustic-based IoT system for condition detection of rotating machines is proposed. The IoT device designed for this purpose is mobile and inexpensive and the algorithm developed for condition detection consists of a combination of discrete wavelet transform and neural networks, while a genetic algorithm is used to tune the necessary hyperparameters. The performance of this system has been tested in a real industrial setting, on different rotating machines, in an environment with strong acoustic pollution. The results show high accuracy of the algorithm, with an average F1 score of around 0.99 with tuned hyperparameters.