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



Manuel Esperon Miguez Adrian Uriondo Jorge Rodriguez Bartolo Luque


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 characterisation 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.

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detection uncertainty, Data Driven, time series, hydraulic system, experimental testing

Aguilar-San Juan , B. & Guzman-Vargas , L., 2013. Earthquake magnitude time series : scaling behavior of visibility networks. Eur Phys J B, p. 86.
Ahmadlou, M. & Adeli, H., 2010. New diagnostic EEG markers of the Alzheimer’s disease using visibility graph. J Neural Transm, p. 117:1099–109.
Benmoussa, S. & Djeziri, M. A., 2017. Remaining useful life estimation without needing for prior knowledge of the degradation features. IET Science, Measurement & Technology, 11(8), p. 1071 – 1078.
Djeziri, M. A., Benmoussa, S. & Sanchez, R., 2018. Hybrid method for remaining useful life prediction in wind turbine systems. Renewable Energy, Volume 116, pp. 173-187.
Elsner, J. B., Jagger, T. H. & Fogarty, E., 2009. Visibility network of United States hurricanes. Geophys Res Lett, p. 36:L16702.
Gao, Z., Cecati, C. & Ding, S. X., 2015. A Survey of Fault Diagnosis and Fault-Tolerant Techniques—Part I: Fault Diagnosis With Model-Based and Signal-Based Approaches. IEEE Transactions on Industrial Electronics, 62(6), pp. 3757 - 3767.
Gutin, G., Mansour, T. & Severini, S., 2011. A characterization of horizontal visibility graphs and combinatorics on words. Phys A Stat Mech Its Appl , p. 390:2421–8.
Jennions, I. K., 2011. Integrated Vehicle Health Management: Perspectives on an Emerging Field. s.l.:SAE International.
Kullback, S. & Leibler, R., 1951. On Information and Sufficiency. Ann Math Stat, p. 22:79–86.
Lacasa, L., Luque, B., Luque, J. & Nuño, J. C., 2009. The visibility graph: A new method for estimating the Hurst exponent of fractional Brownian motion. EPL Europhysics Lett, p. 86:30001.
Lacasa, L., Luque, L., Ballesteros, F. & Nuño, J. C., 2008. From time series to complex networks: the visibility graph. Proc Natl Acad Sci U S A, p. 105:4972–5.
Lacasa, L. et al., 2012. Time series irreversibility: A visibility graph approach. Eur Phys J, p. 85.
Lacasa, L. & Toral, R., 2010. Description of stochastic and chaotic series using visibility graphs. Phys Rev E - Stat Nonlinear, Soft Matter Phys, p. 82.
Luque, B., Lacasa, L., Ballesteros, F. & Luque, J., 2009. Horizontal visibility graphs: Exact results for random time series. Phys Rev E - Stat Nonlinear, Soft Matter Phys , p. 80.
Newman, M., Barabasi, A. L. & Watts, D. J., 2006. The Structure and dynamics of networks. s.l.:Princeton University Press.
Newmann, M. E., 2003. The Structure and Function of Complex Networks. SIAM, p. 45:167–256.
Niculita , O., Skaf, Z. & Jennions, I. K., 2014. The Application of Bayesian Change Point Detection in UAV Fuel Systems. s.l., s.n., p. 22:115–21.
Niculita, O., Irving, P. & Jennions, I. K., 2012. Use of COTS Functional Analysis Software as an IVHM Design Tool for Detection and Isolation of UAV Fuel System Faults. s.l., s.n., pp. vol. 3, p. 13.
Niculita, O., Jennions, I. K. & Irving, P., 2013. Design for diagnostics and prognostics: A physical-functional approach. s.l., s.n., p. 1–15.
Núñez, A. M. et al., 2011. Detecting series periodicity with horizontal visibility graphs. J Bifurc Chaos, p. 1–8.
Sanz-Lobera, A., Gonzalez, I., Rodriguez, J. & Luque, B., 2015. Feasibility Study for Visibility Algorithms Implementation in Surface Texture Characterization. Barcelona, s.n.
Stutz, J., 2010. On Data-centric Diagnosis of Aircraft Systems. IEEE Trans Syst Man Cybern – PART C .
Yawei, G. et al., 2018. A hybrid hierarchical fault diagnosis method under the condition of incomplete decision information system. Applied Soft Computing, Volume 73, pp. 350-365.
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