Large-scale complex systems have the feature of including large amount of variables that have complex relationships, for which signed directed graph (SDG) model could serve as a significant tool by describing the causal relationships among variables. Although qualitative SDG expresses the causing effects between variables easily and clearly, it has many disadvantages or limitations. Probabilistic SDG proposed in the article describes deliver relationships among faults and variables by conditional probabilities, which contains more information and performs more applicability. The article introduces the concepts and construction approaches of probabilistic SDG, and presents the inference approaches aiming at fault diagnosis in this framework, i.e. Bayesian inference with graph elimination or junction tree algorithms to compute fault probabilities. Finally, the probabilistic SDG of a typical example of 65t/h boiler system is given.