用贝叶斯神经网络检测电机故障，解决数据不平衡难题。
Detection of Electric Motor Damage Through Analysis of Sound Signals Using Bayesian Neural Networks
- 采用贝叶斯神经网络处理故障数据不平衡问题。
- 在真实声学信号上验证了故障检测准确率提升。
- 适合工业场景中缺乏均衡数据的设备诊断应用。
故障监测与诊断对保障电动机可靠性至关重要。高效的故障检测算法可提升系统可靠性，但开发成本低且可靠的诊断分类器仍具挑战性，尤其因正常与故障状态信号的平衡数据集难以获取。为此，本文提出使用贝叶斯神经网络来检测并分类电动机故障，因其在处理不平衡训练数据方面表现优异。所提方法在真实声学信号上进行了性能验证，并提供了鲁棒性分析，证明其在实际应用中的有效性。
原文摘要 · Abstract (English)
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.
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