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

Waldemar Bauer () and Jerzy Baranowski ()
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Waldemar Bauer: Department of Automatic Control & Robotics, AGH University of Krakow, 30-059 Krakow, Poland
Jerzy Baranowski: Department of Automatic Control & Robotics, AGH University of Krakow, 30-059 Krakow, Poland

Energies, 2025, vol. 18, issue 18, 1-16

Abstract: Fault detection in electric motors represents a critical challenge across various industries, as failures can lead to substantial operational disruptions. This study examines the application of deep neural networks (DNNs) and Bayesian neural networks (BNNs) for diagnosing motor faults through acoustic signal analysis. We propose a novel approach that leverages frequency-domain representations of sound signals to improve diagnostic accuracy. The architectures of both DNNs and BNNs are developed and evaluated using real-world acoustic data collected from household appliances via smartphones. Experimental results indicate that BNNs achieve superior fault detection performance, particularly in the context of imbalanced datasets, providing more robust and interpretable predictions compared to conventional methods. These findings suggest that BNNs, owing to their ability to incorporate uncertainty, are well-suited for industrial diagnostic applications. Further analysis and benchmarking are recommended to assess the resource efficiency and classification capabilities of these architectures.

Keywords: deep neural networks; Bayesian neural networks; fault detection; acoustic signals; commutator motors (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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