In response to the issues associated with machine learning

such as the requirement for a substantial amount of training data and the relatively poor interpretability of models

this paper integrates rule-based reasoning with a graphical user interface to enhance the comprehensibility and usability of the fault diagnosis system. By employing a hierarchical and graphical representation

the complexity and difficulty of rule formulation are significantly reduced. Particularly

the support for fuzzy mathematical operations allows the rules to adapt more flexibly to various situations

thereby improving the accuracy and reliability of fault diagnosis. Field applications have demonstrated the effectiveness of the methods proposed in this paper

while also effectively enhancing the understanding and formulation capabilities of on-site personnel regarding fault diagnosis rules

thus improving the operational efficiency and safety of the dynamic equipment in the cooling systems of VSC-HVDC.