用物理模型生成故障信号，提升电机诊断准确率。
Learning to Hear Broken Motors: Signature-Guided Data Augmentation for Induction-Motor Diagnostics
- 在频域直接生成符合物理规律的故障数据，无需复杂仿真。
- 新方法使诊断准确率显著提升，适用于工业场景。
- 适合做电机故障检测的工程师和研究人员参考。
机器学习算法在三相电机智能诊断中的应用有望显著提升诊断性能与准确性。传统方法主要依赖于特征分析，尽管是标准实践，但可借助先进机器学习技术进一步优化。本研究创新性地结合机器学习与一种新型无监督异常生成方法，该方法考虑了电机物理模型。我们提出签名引导的数据增强（SGDA），这是一种无监督框架，可在健康电流信号的频域中直接合成物理上合理的故障。该方法基于电机电流特征分析，无需计算量大的模拟即可生成多样且逼真的异常。这种混合方法融合了有监督机器学习与无监督特征分析的优势，在实现更高诊断准确率与可靠性的同时，具备广泛的工业应用潜力。结果表明，该方法对电机诊断领域具有重要贡献，为实际应用提供了稳健高效的解决方案。
原文摘要 · Abstract (English)
The application of machine learning (ML) algorithms in the intelligent diagnosis of three-phase engines has the potential to significantly enhance diagnostic performance and accuracy. Traditional methods largely rely on signature analysis, which, despite being a standard practice, can benefit from the integration of advanced ML techniques. In our study, we innovate by combining ML algorithms with a novel unsupervised anomaly generation methodology that takes into account the engine physics model. We propose Signature-Guided Data Augmentation (SGDA), an unsupervised framework that synthesizes physically plausible faults directly in the frequency domain of healthy current signals. Guided by Motor Current Signature Analysis, SGDA creates diverse and realistic anomalies without resorting to computationally intensive simulations. This hybrid approach leverages the strengths of both supervised ML and unsupervised signature analysis, achieving superior diagnostic accuracy and reliability along with wide industrial application. The findings highlight the potential of our approach to contribute significantly to the field of engine diagnostics, offering a robust and efficient solution for real-world applications.
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