Fault analysis at the early design stages of an engineering system is crucial for ensuring reliability and safety during operation. Given the limited information on system components and configurations available at this stage, such analysis heavily relies on historical data and expert knowledge. Traditional methods like Fault Tree, Bayesian networks, and Markov chains depend on manually established causality models for system failures. In complex systems with numerous components, creating these causality models becomes increasingly time-consuming and susceptible to human-error in identifying potential causal relationships. One of the major reasons for the modeling errors is that the causality models lack the support of physics. To address these limitations, this article introduces a novel approach for formally establishing causal relations between faults and system failures and calculating the probabilities of each cause. Even at conceptual design stage, the proposed method can automatically deduce all possible causes and fault propagation paths for each system failure corresponding to the physics modeled by the analyst. The entire approach is divided into two major steps: the first step identifies the system trajectories for a known condition using qualitative physics and symbolic AI, and the second step calculates the conditional probabilities of the causes outlined in the first step for a given initial condition. Knowledge about the probabilistically weighted causes of system failures allows designers to identify potential issues that have a relatively high likelihood of occurrence and severe consequences. The article demonstrates the method’s application by analyzing a simplified secondary loop of a nuclear power plant.
