Scheduled maintenance and condition-based online monitoring are among the focal points of recent research to enhance nuclear plant safety. One of the most effective ways to monitor plant conditions is by imple-menting a full-scope, plant-wide fault diagnostic system. However, most of the proposed diagnostic techniques are perceived as unreliable by operators because they lack an explanation module, their implementation is complex, and their decision/inference path is unclear. Graphical formal-ism has been considered for fault diagnosis because of its clear decision and inference modules, and its ability to display the complex causal relationships between plant variables and reveal the propagation path used for fault localization in complex systems. However, in a graph-based approach, decision-making is slow because of rule explosion. In this paper, we present an enhanced signed directed graph that utilizes qualitative trend evaluation and a granular computing algorithm to improve the decision speed and increase the resolution of the graphical method. We integrate the attribute reduction capability of granular computing with the causal/fault propagation reasoning capability of the signed directed graph and comprehensive rules in a decision table to diagnose faults in a nuclear power plant. Qualitative trend analysis is used to solve the problems of fault diagnostic threshold selection and signed directed graph node state determination. The similarity reasoning and detection ability of the granular computing algorithm ensure a compact decision table and improve the decision result. The performance of the proposed enhanced system was evaluated on selected faults of the Chinese Fuqing 2 nuclear reactor. The proposed method offers improved diagnostic speed and efficient data processing. In addition, the result shows a considerable reduction in false positives, indicating that the method provides a reliable diagnostic system to support further intervention by operators.