This paper presents a state-of-the-art review on machine learning (ML) based intelligent diagnostics that have been applied for partial discharge (PD) detection, localization, and pattern recognition. ML techniques, particularly those developed in the last five years, are examined and classified as conventional ML or deep learning (DL). Important features of each method, such as types of input signal, sampling rate, core methodology, and accuracy, are summarized and compared in detail. Advantages and disadvantages of different ML algorithms are discussed. Moreover, technical roadblocks preventing intelligent PD diagnostics from being applied to industry are identified, such as insufficient/imbalanced dataset, data inconsistency, and difficulties in cost-effective real-time deployment. Finally, potential solutions are proposed, and future research directions are suggested.

中文翻译：

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使用机器学习方法基于局部放电诊断的状态监测：全面的最新技术回顾

本文介绍了基于机器学习 (ML) 的智能诊断的最新评论，该诊断已应用于局部放电 (PD) 检测、定位和模式识别。ML 技术，特别是过去五年开发的技术，被检查并归类为传统 ML 或深度学习 (DL)。每种方法的重要特征，如输入信号类型、采样率、核心方法和准确性，都进行了详细的总结和比较。讨论了不同 ML 算法的优缺点。此外，还发现了阻碍智能局部放电诊断应用于工业的技术障碍，例如数据集不足/不平衡、数据不一致以及难以经济高效的实时部署。最后，提出了潜在的解决方案，