Remaining Useful Life Prognostics Using Pattern-Based Machine Learning

This paper presents a prognostic methodology that can be implemented in a condition-based maintenance (CBM) program. The methodology estimates the remaining useful life (RUL) of a system by using a pattern-based machine learning and knowledge discovery approach called Logical Analysis of Data (LAD). The LAD approach is based on the exploration of the monitored system's database, and the extraction of useful information which describe the physics that characterize its degradation. The diagnostic information, which is updated each time the new data is gathered, is combined with a non-parametric reliability estimation method, in order to predict the RUL of a monitored system working under different operating conditions. In this paper, the developed methodology is compared to a known CBM prognostic technique; the Cox proportional hazards model (PHM). The methodology has been tested and validated based on the Friedman statistical test. The results of the test indicate that the proposed methodology provides an accurate RUL prediction.