Reeves, Jack David
(2018)
Sensor selection for fault diagnostics.
PhD thesis, University of Nottingham.
Abstract
In the modern world, systems such as aircraft systems are becoming increasingly complex, often consisting of a large number of components. As no component is perfectly reliable, they can fail, some in many different ways, leading to a large number of potential component failures on complex systems. Component failures can have detrimental effects on the performance of the system, with some component failures even causing system failure, potentially damaging the system, or more importantly, potentially endangering human life. In order to be able to detect component failures on complex systems, the inclusion of sensors is becoming increasingly common. In addition to being able to detect component failures, the sensors can be used to diagnose component failures, with certain symptoms and the resultant sensor readings being produced by certain component failures. Another benefit is that it may also be possible to prevent system failure by detecting component failures early, activating redundant components and enabling the mission to be completed. However, including sensors in the system increases the cost of the system, can add weight to the system and require space for installation, a factor of particular importance for weight critical systems, such as aircraft systems. Therefore, a balance between being able to detect and diagnose failures in systems and the cost, weight and space requirements of the sensors needs to be achieved.
In this thesis, a novel sensor selection methodology is proposed, which is based on a performance metric. Individual sensors, and combinations of sensors are ranked based on their performance of detecting faults and diagnosing failures in the system. In addition to the sensors’ detection and diagnostic performance, the metric also considers the effect that the component failures have on the functionality of the system, where sensors that detect critical failures are favoured over sensors that do not detect such failures. The performance metric is then extended to consider the time taken to detect and diagnose component failures, as the sooner component failures are detected, the more likely system failure can be prevented. This is important in a system that operates in a phased mission. In addition, a proposed two-level Genetic Algorithm is used in order to efficiently determine a suitable combination of sensors for larger systems, where an exhaustive calculation of the performance metric for all combinations of sensors is not feasible.
For a simple flow system, a Bayesian Belief Network (BBN) is used to model the effects of component failures, and sensor readings. During the fault diagnostic process, observed sensor readings can be introduced in the BBN, which then can be used to identify the failed components. However, an alternative system modelling and fault diagnostic technique is proposed as a part of this thesis which can be used on larger systems, and can determine sensor readings and component failures more quickly than the BBN method. This method is based on a series of if-then-else statements in order to determine the effect that the component states have on the performance of the system. The work proposed in this thesis is applied to three example systems: a simple flow system, an example aircraft fuel system and the fuel system for an Airbus A380-800 aircraft.
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