No abstract

In the modern world, systems are becoming increasingly complex, consisting of large numbers of components and their failures. In order to monitor system performance and to detect faults and diagnose failures, sensors can be used. However, using sensors can increase the cost and weight of the system. Therefore, sensors need to be selected based on the information that they provide.In this paper, a sensor selection process is introduced based on a novel sensor performance metric. In this process, sensors are selected based on their ability to detect faults and diagnose failures of components in the system, as well as the severity of failure effects on system performance. A Bayesian Belief Network (BBN) is used to model the outputs of the sensors. Sensor reading evidence is introduced in the BBN to enable the component failures to be identified. A simple system example is used to illustrate the proposed approach.

Component failures in complex systems, such as aircraft fuel systems, can have catastrophic effects on system performance. There are a large number of components in these systems, each with a number of different failure modes, some of which can cause system failure. In order to detect and diagnose these component failures, sensors that monitor system performance need to be included. However, the number of sensors installed is typically limited by sensor cost and weight. An approach for selecting sensors could be taken considering sensor usefulness for fault diagnostics. In this paper, the sensor performance metric proposed by Reeves et al. [1] is extended to consider a phased mission operation, with component failures occurring at various points in the mission. The performance metric favours sensors that can detect the most failures, the failures that affect the system for longest and the failures that cause system failure. In addition, the performance metric considers the ability of sensors to distinguish between component failures, i.e. to diagnose which components have caused the faults observed by these sensors. The proposed approach is illustrated on the Airbus A380-800 fuel system, where the best combination is found using the performance metric within a Genetic Algorithm method.

Variable Frequency AC (VFAC)

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.