Abstract:

Fault diagnostics systems are incorporated to determine the health of the system they monitor. There are however times when the diagnostics system reports faults which do not exist. This situation commonly arises at system start-up when high vibration levels exist and the systems are not performing in the same way as when they are operational. Unnecessary shutdowns can occur due to transient behaviour of the system. On autonomous vehicles, such as Unmanned Aerial Vehicles (UAVs), information about the health of the system can be used to support the decision making process and to plan the future system operation. When faults are reported on autonomous systems, where there is no pilot to interpret the conditions reported, a method is needed to establish whether the reported faults do exist. Utilizing a fault propagation modeling technique deviations in system variables can be propagated through the system until further evidence of fault presence is observed. If some evidence that contradicts the fault presence is found, the fault can be cancelled and unnecessary shutdowns can be avoided.

In this paper a propagation table method is developed to model fault propagation through a system. The system is broken down into its constituent components and each model shows how process variables depend not only on the state of the component but also on the state of the entire system. The outputs of the two-way fault propagation modeling are values of process variables at different locations in the system. These values can be compared with the symptoms observed and used to cancel or confirm faults. This comparison process is accomplished at each phase that the system goes through during its defined mission. The illustration of the fault propagation methodology is given using an example system, and its application for the fault cancellation process is discussed.
Received on October 27, 2009, revised on August 04, 2010
References: 14