Health management systems are now standard aspects of complex systems. They monitor the behaviour of components and sub-systems and in the event of unexpected system behaviour diagnose faults that have occurred. Although this process should reduce system downtime, it is known that health management systems can generate false faults that do not represent the actual state of the system and cause resources to be wasted. The authors propose a process to address this issue in which Petri nets (PNs) are used to model complex systems. Faults reported on the system are simulated in the PN model to predict the resultant system behaviour. This behaviour is then compared to that from the actual system. Using the standard deviation technique, the similarity of the system variables is assessed and the validity of the fault determined. The process has been automated and is tested through application to an experimental rig representing an aircraft fuel system. The success of the process to verify genuine faults and identify false faults in a multi-phase mission is demonstrated. A technique is also presented that is specific to tank leaks where depending on the location and size of the leak, the resulting symptoms will vary. Copyright © 2012 John Wiley & Sons, Ltd.