Aircraft fuel rig system fault diagnostics based on the application of digraphs

Abstract

The manuscript was received on 3 May 2007 and was accepted after revision for publication on 25 July 2007. DOI: 10.1243/1748006XJRR88Abstract: The issue of fault diagnostics is a dominant factor concerning current engineering systems. Information regarding possible failures is required in order to minimize disruption caused to functionality. A method proposed in this paper utilizes digraphs to model the information flow within an application system. Digraphs are composed from a set of nodes representing system process variables or component failure modes. The nodes are connected by signed edges thus illustrating the influence, be it positive or negative, one node has on another. System fault diagnostics is conducted through a procedure of back-tracing in the digraph from a known deviating variable. A computational method has been developed to conduct this process. Comparisons are made between retrieved transmitter readings and those expected while the system is in a known operating mode. Any noted deviations are assumed to indicate the presence of a failure. The current paper looks in detail at the application of the digraph diagnostic method to an industrially based test stand of an aircraft fuel system. This research includes transient system effects; the rate of change of a parameter is taken into consideration as a means of monitoring the system dynamically. The validity of the results achieved, through performing fault diagnostics based on the use of a digraph model, is evaluated. Finally, the effectiveness and scalability issues associated with the application of the method are addressed.

A Generalised Methodology for the Diagnosis of Aircraft Systems

“…The possibility of faults like pump failures, leakage in the fuel pipes, clogging in the filters, sticking valves, and blockages are taken care of during maintenance in order to prevent their occurrence in flight. There are several diagnostic methods developed to isolate faults to the LRU level to help with troubleshooting activities [27]- [30].…”

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.…”