Abstract
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 article, 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 is developed. Sensors are selected using the performance metric, and sensor readings can be introduced in the Bayesian Belief Network. The results of the Bayesian Belief Network 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 article.
Highlights
Aircraft systems, such as fuel systems, can be very complex, since they typically consist of a large number of different components, each of which can fail, sometimes in more than one failure mode
This considers the probability of the component failure occurrence which can be detected by the sensors, how easy it is to diagnose the failures, and what effect the component failures have on system performance, for example, how likely they are to cause the system to fail
The performance metric was calculated for all combinations of one, two, three and four sensors in this example
Summary
Aircraft systems, such as fuel systems, can be very complex, since they typically consist of a large number of different components, each of which can fail, sometimes in more than one failure mode. In order to be able to monitor system performance and detect early signs of system failure, sensors can be used on the system. These sensors can be all of one type, or a combination of multiple different types, such as flow, pressure and level sensors on a fuel system. If the sensors can be used to diagnose a component failure, a required action can be undertaken . For aircraft systems, this could be to abort the mission if it is unsafe for the aircraft to continue, adapt the mission if the aircraft is still safe to operate, but unable to complete the originally planned mission, or to continue the mission as normal if the component failures have no detrimental effect on the operation of the aircraft. If the mission does not have to be aborted, knowing component failures can help to plan the required maintenance work to be undertaken at the scheduled stop, reducing the unplanned down-time for the aircraft
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