基于贝叶斯神经网络的声音信号分析用于电动机损伤检测
Detection of Electric Motor Damage Through Analysis of Sound Signals Using Bayesian Neural Networks
AI总结：
针对电动机故障诊断中均衡数据集难以获取、分类器开发难的问题，提出利用贝叶斯神经网络分析声音信号检测电动机损伤，在真实信号上验证了性能并完成鲁棒性分析。
摘要：
故障监测与诊断对保障电动机的可靠性至关重要。高效的故障检测算法可提升可靠性，但开发兼具成本效益与可靠性的设备诊断分类器颇具挑战，这主要是因为难以获取包含正常运行设备信号与故障设备信号的均衡数据集。为此，我们提出采用贝叶斯神经网络（Bayesian neural network）来检测并分类电动机的故障，该方法在处理非均衡训练数据方面具备有效性。我们在真实场景的信号上验证了所提网络的性能，并对该解决方案开展了鲁棒性分析。
英文摘要：
Fault monitoring and diagnostics are important to ensure reliability of electric motors. Efficient algorithms for fault detection improve reliability, yet development of cost-effective and reliable classifiers for diagnostics of equipment is challenging, in particular due to unavailability of well-balanced datasets, with signals from properly functioning equipment and those from faulty equipment. Thus, we propose to use a Bayesian neural network to detect and classify faults in electric motors, given its efficacy with imbalanced training data. The performance of the proposed network is demonstrated on real life signals, and a robustness analysis of the proposed solution is provided.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
