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

TL;DR

The document presents elsarticle.cls, a LaTeX document class designed to format manuscripts for Elsevier journals. It explains the architecture, dependencies, and design choices that minimize conflicts with common TeX packages while supporting flexible front matter and formatting options. Key contributions include a detailed feature overview, a comparison with the predecessor elsart.cls, and a practical installation workflow via CTAN and Elsevier resources. The class simplifies preparing submissions by providing robust, configurable layouts and integration with BibTeX/natbib, hyperref, and related tools, improving consistency and reducing setup effort for authors.

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.