Smartphone-Based Bearing Fault Diagnosis in Rotating Machinery Using Audio Data and 1d Convolutional Neural Networks

Abstract

Asynchronous machines are essential components that drive critical systems across industrial, trading, and residential sectors, powering heating units, pumps, and various appliances. Yet, ensuring their reliable process is paramount to prevent costly defects and maintain productivity. Notably, failures in the rolling element bearings (REB) account for about forty percent of motor failures, underscoring the urgency of early detection to mitigate operational risks and financial losses. To address this challenge, this paper proposes an innovative smartphone-based diagnostic technique for detecting bearing faults in induction machines. Leveraging the common availability and computational capabilities of smartphones, the approach utilizes the devices' audio recording functionality to capture motor audio signals. Audio data collected from rotating machines with various fault types is used to train a 1D Convolutional Neural Network (1D CNN), and the trained model is then deployed on a smartphone for real-time fault diagnosis. Embedding this approach into a user-friendly mobile application enhances accessibility and usability, offering a cost-effective solution for fault diagnosis in induction machines. © 2024 ACM.