A fault mode identification methodology based on self-organizing map

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; Svärd 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.