A Feasibility Study on the Implementation of Visibility Algorithms for Fault Diagnosis in Aircraft Fuel Systems

Abstract

This paper discusses the applicability of Visibility Algorithms to detect faults in condition monitoring applications. The general purpose of Visibility Algorithms is to transform time series into graphs and study them through the characterization of their associated network. Degradation of a component results in changes to the network. This technique has been applied using a test rig of an aircraft fuel system to show that there is a correlation between the values of key metrics of visibility graphs and the severity of four failure modes. We compare the results of using Horizontal Visibility algorithms against Natural Visibility algorithms. The results also show how the Kullback-Leibler divergence and statistical entropy can be used to produce condition indicators. Experimental results show that there is little dispersion in the values of condition indicators, leading to a low probability of false positives and false negatives.

Intelligent Fault Diagnosis of an Aircraft Fuel System Using Machine Learning—A Literature Review

“…It is easier to discover the nature of problems and the correlation between factors, e.g., Ref. [38] used a simplified fuel rig to determine the best indicator of each degradation mode within their research and the correlation between those indicators.…”

Signal Processing of Acoustic Data for Condition Monitoring of an Aircraft Ignition System

“…The average number of connected nodes found using a visibility graph (µ HVG ). The horizontal visibility graph was chosen for evaluation, in which two nodes (x i and x j ) are said to be connected only if a horizontal line can be drawn between them that does not intersect any of the intermediate data points [35]:…”