Reducing arbitrary choices in model building for prognostics: An approach by applying parsimony principle on an evolving neuro-fuzzy system

Failure prognostics requires an efficient prediction tool to be built. This task is as difficult as, in many cases, very few knowledge or previous experiences on the degradation process are available. Following that, practitioners are used to adopt a “trial and error” approach, and to make some assumptions when developing a prediction model: choice of an architecture, initialization of parameters, learning algorithms… This is the problem addressed in this paper: how to systematize the building of a prognostics system and reduce the influence of arbitrary human intervention? The proposition is based on the use of a neuro-fuzzy predictor whose structure is partially determined, on one side, thanks to its evolving capability, and on the other side, thanks to parsimony principle. The aim of the approach is to automatically generate a suitable prediction system that reaches a compromise between complexity and accuracy capability. The whole proposition is illustrated on a real-world prognostics problem concerning the prediction of an engine health.