Fault tree based fault diagnostics for dynamic systems

For conventional systems, their availability can be considerably improved by reducing the time taken to restore the system to the working state when faults occur. Fault identification can be a significant proportion of the time taken in the repair process. Having diagnosed the problem the restoration of the system back to its fully functioning condition can then take place. For autonomous systems, such as UAVs, the fault detection system takes on an even more significant role in providing an input to the system control decision making process. Known failures are used to update the predictions on the likelihood of mission success. Unacceptable probabilities of mission success result in redefining or abandoning the mission. This paper expands the capability of previous approaches to fault detection and identification using fault trees for application to dynamically changing systems. The technique has two phases. The first phase is the modelling and preparation phase carried out off-line. This gathers information on the effects that subsystem failure will have on the system performance. Causes of the subsystem failures are developed in the form of fault trees. The second phase is the application phase. Sensors are installed on the system to provide information about current system performance from which the potential causes can be deduced. A simple system example is used to demonstrate the features of the method.