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 state of the art algorithms with a novel unsupervised anomaly generation methodology that takes into account physics model of the engine. This hybrid approach leverages the strengths of both supervised ML and unsupervised signature analysis, achieving superior diagnostic accuracy and reliability along with a wide industrial application. Our experimental results demonstrate that this method significantly outperforms existing ML and non-ML state-of-the-art approaches while retaining the practical advantages of an unsupervised methodology. The findings highlight the potential of our approach to significantly contribute to the field of engine diagnostics, offering a robust and efficient solution for real-world applications.

翻译：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精度。传统方法主要依赖特征分析，尽管这是标准实践，但结合先进的机器学习技术可使其获益。本研究创新性地将前沿算法与一种新颖的无监督异常生成方法相结合，该方法考虑了电机的物理模型。这种混合方法融合了监督式机器学习与无监督特征分析的优势，在实现卓越诊断准确性和可靠性的同时，具备广泛的工业适用性。实验结果表明，该方法在保持无监督方法实用优势的同时，显著优于现有的机器学习与非机器学习前沿方法。研究结果凸显了本方法对电机诊断领域的重要贡献潜力，为实际应用提供了稳健高效的解决方案。