System Health Prognostic Model Using Rough Sets

Abstract

A new rough sets data fusion model is presented fusing measured health degradation levels and influences on these degradations. The data fusion model is a system of matrix inequalities of the rough sets covariances. Rough sets variance allows to explicitly assess only health degradations assuring increased signal-to-noise ratio, thus high accuracy of processing. The matrices of inequalities fuse measured health degradation levels and influences on these degradations. Adaptations mechanisms are by a new machine learning approach determining weights of the terms of the inequalities at the time of key events found in the historical data. Prognostic is always time-sequenced, therefore methods based on time sequences are incorporated, e.g. a new data fusion model exploiting time-dependency of events, assuring high quality of prediction. Deterministic prognostic is by estimating the pattern of health degradation in question, finding the match with degradation pattern in historical data, and then tracing this historical degradation pattern up to its conclusion. The model is hierarchical: the right sides of the data fusion expressions substitute for endogenous variables of higher-level expressions.