Comparison of Deep Recurrent Neural Networks and Bayesian Neural Networks for Detecting Electric Motor Damage Through Sound Signal Analysis

TL;DR

This paper compares Recurrent Neural Networks and Bayesian Neural Networks for detecting electric motor faults through sound analysis, showing BNNs offer more robust and interpretable diagnostics especially with imbalanced data.

Contribution

It introduces a novel frequency domain approach and evaluates RNNs and BNNs on real-world acoustic data, highlighting the superior performance of BNNs in fault detection.

Findings

BNNs outperform RNNs in fault detection accuracy

BNNs provide more robust predictions with imbalanced datasets

Frequency domain representation improves diagnostic performance

Abstract

Fault detection in electric motors is a critical challenge in various industries, where failures can result in significant operational disruptions. This study investigates the use of Recurrent Neural Networks (RNNs) and Bayesian Neural Networks (BNNs) for diagnosing motor damage using acoustic signal analysis. A novel approach is proposed, leveraging frequency domain representation of sound signals for enhanced diagnostic accuracy. The architectures of both RNNs and BNNs are designed and evaluated on real-world acoustic data collected from household appliances using smartphones. Experimental results demonstrate that BNNs provide superior fault detection performance, particularly for imbalanced datasets, offering more robust and interpretable predictions compared to traditional methods. The findings suggest that BNNs, with their ability to incorporate uncertainty, are well-suited for industrial diagnostic applications. Further analysis and benchmarks are suggested to explore resource efficiency and classification capabilities of these architectures.