Online monitoring of gases dissolved in transformer oil is widely applied. Improving the performance of dissolved gas analysis (DGA)-based fault diagnosis methods by exploring new features of time-series data has become an appealing topic. In this study, a new type of correlation features between characteristic gases was extracted from time-series data based on the maximal information coefficient (MIC), and a fuzzy inference system was established. After the introduction of the principle of the MIC and a method for calculating the MIC-based correlation features, the dominant symptom features that can be used to classify fault types were extracted through the receiver operating characteristic curve. Then, fuzzy rules were learnt, and a fuzzy inference system was designed. In addition, to improve the feasibility of the method, the Newton interpolation method was used for adaptation to the existing sampling cycle. The diagnostic results of the test data show that the proposed method has excellent performance and outperforms some prevailing traditional rule-based methods as well as some artificial intelligent methods. The results also show that by exploring new correlation features from time-series data based on the MIC, the performance of DGA-based methods can be improved.

中文翻译：

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基于最大信息系数的基于溶解气体分析的变压器故障诊断新关联特征

变压器油中溶解气体的在线监测应用广泛。通过探索时间序列数据的新特征来提高基于溶解气体分析 (DGA) 的故障诊断方法的性能已成为一个吸引人的话题。本研究基于最大信息系数（Maximal Information Coefficient，MIC）从时间序列数据中提取一种新型特征气体之间的相关特征，并建立了模糊推理系统。在介绍了MIC的原理和基于MIC的相关特征的计算方法之后，通过接收者操作特征曲线提取了可用于分类故障类型的主要症状特征。然后，学习了模糊规则，设计了模糊推理系统。此外，为了提高方法的可行性，牛顿插值法用于适应现有的采样周期。测试数据的诊断结果表明，该方法具有优异的性能，优于一些流行的传统基于规则的方法以及一些人工智能方法。结果还表明，通过从基于 MIC 的时间序列数据中探索新的相关特征，可以提高基于 DGA 的方法的性能。