A fault mode identification methodology based on self-organizing map
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ORIGINAL ARTICLE
A fault mode identification methodology based on self-organizing map Se´bastien Schwartz1,2
•
Juan Jose´ Montero Jimenez2,3 • Michel Salau¨n2 • Rob Vingerhoeds2
Received: 14 January 2019 / Accepted: 18 December 2019 Springer-Verlag London Ltd., part of Springer Nature 2020
Abstract One of the main goals of predictive maintenance is to be able to trigger the right maintenance actions at the right moment in time building upon the monitoring of the health status of the concerned systems and their components. As such, it allows identifying incipient faults and forecasting the moment of failure at the earliest stage. Many different data-driven methods are used in such approaches (Naderi and Khorasani in 2017 IEEE 30th Canadian conference on electrical and computer engineering (CCECE), Windsor, ON, IEEE, pp 1–6, 2017. https://doi.org/10.1109/ccece.2017.7946715; Sarkar et al. in J Eng Gas Turbines Power 1338(8):081602, 2011. https://doi.org/10.1115/1.4002877; Sva¨rd et al. in Mech Syst Signal Process 45(1):170–192, 2014. https://doi.org/10.1016/j.ymssp.2013.11.002; Pourbabaee et al. Mech Syst Signal Process 76–77:136–156, 2016. https://doi.org/10.1016/j.ymssp.2016.02.023). This work uses the self-organizing maps (SOMs) or Kohonen map, thanks to its ability to emphasize underlying behavior such as fault modes. An automatic fault mode detection is presented based on a SOM network and the kernel density estimation with as less as possible prior knowledge. The different SOM development steps are presented and the suitable solutions proposed to structure the approach are accompanied by mathematical methods. The generated maps are then used with kernel density analysis to isolate fault modes on them. Finally, a methodology is presented to identify the different fault modes. The work is illustrated with an aircraft jet engines case study. Keywords Diagnostic Fault identification Predictive maintenance Self-organizing map
1 Introduction Maintenance departments are confronted with three types of maintenance: corrective maintenance (i.e., correcting systems that break down or have a deteriorated functional behavior), preventive maintenance (i.e., maintenance actions at regular intervals, to avoid break down or deterioration) and predictive maintenance (i.e., performing specific maintenance actions based on indications derived & Se´bastien Schwartz [email protected] 1
SOGETI High Tech, R&D Dpt., Aeropark, 3 Chemin de Laporte, 31100 Toulouse, France
2
ISAE-SUPAERO, Universite´ de Toulouse, 10 Avenue Edouard Belin, 31400 Toulouse, France
3
Tecnolo´gico de Costa Rica Institute of Technology, Calle 15, ´ ngeles, Avenida 14., 1 km Sur de la Bası´lica de los A Provincia de Cartago, Cartago 30101, Costa Rica
from fine analysis on data, crew reports, etc.). Predictive maintenance has seen a huge rise over the last years, essentially due to the application of neural networks to identify incipient faults and to forecast the moment of failure thro
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