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.

翻译：故障监测与诊断对确保电机可靠性至关重要。高效的故障检测算法可提升可靠性，但开发经济可靠且适用于设备诊断的分类器仍具挑战性，主要原因是缺乏平衡数据集——既包含正常运行设备信号又涵盖故障设备信号。因此，我们提出采用贝叶斯神经网络进行电机故障检测与分类，该方法在处理不平衡训练数据方面具有显著优势。通过实际信号验证了所提网络的性能，并对解决方案进行了鲁棒性分析。