Enhanced diagnosis of faults using the digraph approach applied to a dynamic aircraft fuel system

Abstract

This item was submitted to Loughborough's Institutional Repository (https://dspace.lboro.ac.uk/) by the author and is made available under the following Creative Commons Licence conditions.For the full text of this licence, please go to: http://creativecommons.org/licenses/by-nc-nd/2.5/ Enhanced diagnosis of faults using the digraph approach applied to a dynamic aircraft fuel system E M Kelly and L M Bartlett* Department of Aeronautical and Automotive Engineering, Loughborough University, Loughborough, Leicestershire, UKThe manuscript was received on 1 October 2007 and was accepted after revision for publication on 5 August 2008. DOI: 10.1243/1748006XJRR119Abstract: Malfunctions within commercial aircraft can considerably increase both the financial cost of downtime and the disruption caused to passenger travel. For this reason prompt detection, diagnosis, and rectification of faults is imperative to the successful operation of such a system. In this paper the fault diagnostic problem is tackled based on the application of the digraph procedure. Digraphs model the information flow, and hence fault propagation, through a system. A computational method has been successfully developed to conduct the fault diagnostics process and produce a list of the fault combinations determined. The scope of the method has been demonstrated by consideration of two modes of operation to the application of a commercial aircraft fuel system, namely that of a Boeing 777. In addition the paper highlights the contribution of the development of a reduction method to enhance the likelihood of identifying the possible failure causes in three ways from different viewpoints. The three methods provide the option of determining the component at fault, the most probable failure mode cause, and also evidence for a particular component fault.

Data mining–based intelligent fault diagnostics for integrated system health management to avionics

“…14 DM, especially in the mining association rules (MARs), performs well in association with analysis, factor analysis, and FD. 12,19,20 In Dou et al, 10 a new method for intelligent fault identification was proved to be a convenient, concise, interpretable, and reliable way to diagnose faults. Yang et al 12 presented a novel association rule mining (ARM)-based approach to FD which resulted in an accuracy improvement.…”