用声音检测电机故障，贝叶斯神经网络更准更可靠
Comparison of Deep Recurrent Neural Networks and Bayesian Neural Networks for Detecting Electric Motor Damage Through Sound Signal Analysis
- 用频域声音信号分析电机损伤，对比了RNN与贝叶斯神经网络
- 贝叶斯网络在不平衡数据上表现更优，准确率更高且可解释性更强
- 适合工业场景中需要可信诊断的故障检测任务
电机故障检测是多个行业中的关键挑战，故障可能导致重大运营中断。本研究探讨了利用声学信号分析进行电机损伤诊断的循环神经网络（RNN）与贝叶斯神经网络（BNN）方法。提出一种新方法，通过声音信号的频域表示提升诊断准确性。RNN与BNN架构在使用智能手机采集的真实家用电器声学数据上进行了设计与评估。实验结果表明，BNN在不平衡数据集上表现出更优的故障检测性能，提供更稳健、可解释的预测，优于传统方法。研究认为，具备不确定性建模能力的BNN更适合工业诊断应用。建议进一步分析和基准测试以探索两类架构在资源效率与分类能力方面的表现。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。