Signal-Flow-Based Graphs for Failure-Mode Analysis of Systems with Control Loops

Control loops make failure-mode analysis via fault trees extremely difficult. This paper proposes a new approach based on signal flow graphs to model systems with control loops. Mason´s Rule is applied to assess the effect of the loops. The top event of the system is defined by an inequality on a node-variable of the signal flow graph. Basic failures are modeled by source variables. Cut-off failures of the control loops are also considered. The method is useful for uncovering failure modes leading to the top event in complicated systems with control loops. General steps to apply the method to a system are: 1. Draw a SFG for the system. 2. Model basic failures by source variables. 3. Select a node-variable to define a top event. 4. Represent the top event in terms of the source variables, using Mason´s Rule. 5. Discretize the source variables. 6. Classify loop states. 7. For each loop state, obtain system failure modes, using a search tree like Fig. 5. 8. Review the failure modes by more accurate simulation models. Any model is an approximation of an actual system. Thus, the resulting failure modes like those in Table 3 should be examined again, using past experience, more accurate simulation models, etc. The method should be viewed primarily as useful tool for uncovering failure modes in which complicated systems with the control loops fail.