System level airborne avionics prognostics for maintenance, repair and overhaul

• Main Author: Aman Shah, Shahani
• Other Authors: Jia, Huamin
• Language: en
• Published: Cranfield University 2017
• Subjects: Aircraft maintenance; Prognostics in avionics; Enhanced ground proximity warning system
• Online Access: http://dspace.lib.cranfield.ac.uk/handle/1826/11844

The aim of this study is to propose an alternative approach in prognostics for airborne avionics system in order to enhance maintenance process and aircraft availability. The objectives are to analyse the dependency of avionic systems for fault propagation behaviour degradation, research and develop methods to predict the remaining useful life of avionics Line Replaceable Units (LRU), research and develop methods to evaluate and predict the degradation performances of avionic systems, and lastly to develop software simulation systems to evaluate methods developed. One of the many stakeholders in the aircraft lifecycle includes the Maintenance, Repair and Overhaul (MRO) industry. The predictable logistics process to some degree as an outcome of IVHM gives benefit to the MRO industry. In this thesis, a new integrated numerical methodology called ‘System Level Airborne Avionic Prognostics’ or SLAAP is developed; looking at a top level solution in prognostics. Overall, this research consists of two main elements. One is to thoroughly understand and analyse data that could be utilised. Secondly, is to apply the developed methodology using the enhanced prognostic methodology. Readily available fault tree data is used to analyse the dependencies of each component within the LRUs, and performance were simulated using the linear Markov Model to estimate the time to failure. A hybrid approach prognostics model is then integrated with the prognostics measures that include environmental factors that contribute to the failure of a system, such as temperature. This research attempts to use data that is closest to the data available in the maintenance repair and overhaul industry. Based on a case study on Enhanced Ground Proximity Warning System (EGPWS), the prognostics methodology developed showed a sufficiently close approximation to the Mean Time Before Failure (MTBF) data supplied by the Original Equipment Manufacturer (OEM). This validation gives confidence that the proposed methodology will achieve its objectives and it should be further developed for use in the systems design process.