A neuro-fuzzy approach for estimating mean residual life in condition-based maintenance systems

This paper presents a framework for online reliability estimation of physical systems utilising degradation signals. Most prognostics methods promoted in the literature for estimation of mean-residual-life of individual components utilise trending or forecasting models in combination with mechanistic or empirical failure definition models. In the absence of sound knowledge for the mechanics of degradation and/or adequate failure data, it is not possible to establish practical failure definition models. However, if there exist domain experts with strong experiential knowledge, one can establish fuzzy inference models for failure definition. This paper presents a neuro-fuzzy approach for performing prognostics under such circumstances. The proposed approach is evaluated on a cutting tool monitoring problem. In particular, the method is used to monitor high-speed-steel drill-bits used for drilling holes in stainless steel metal plates.