A Petri net approach to fault verification in phased mission systems using the standard deviation technique

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 are used to model complex systems. Faults reported on the system are simulated in the Petri net 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

Sensor selection for fault diagnostics using performance metric

As technology advances, modern systems are becoming increasingly complex, consisting of large numbers of components, and therefore large numbers of potential component failures. These component failures can result in reduced system performance, or even system failure. The system performance can be monitored using sensors, which can help to detect faults and diagnose failures present in the system. However, sensors increase the weight and cost of the system, and therefore, the number of sensors may be limited, and only the sensors that provide the most useful system information should be selected.In this paper, a novel sensor performance metric is introduced. This performance metric is used in a sensor selection process, where the sensors are chosen based on their ability to detect faults and diagnose failures of components, as well as the effect the component failures have on system performance. The proposed performance metric is a suitable solution for the selection of sensors for fault diagnostics. In order to model the outputs that would be measured by the sensors, a Bayesian Belief Network (BBN) is developed. Sensors are selected using the performance metric, and sensor readings can be introduced in the BBN. The results of the BBN can then be used to rank the component failures in order of likelihood of causing the sensor readings. To illustrate the proposed approach, a simple flow system is used in this paper