Abstract

The power generator is typically maintained with a time- or usage-based strategy, which could result in a substantial waste of remaining useful life, high maintenance cost, and low plant availability. Recently, the field of prognostics and health management offers diagnostic and prognostic techniques to precisely assess the health condition and robustly predict the remaining useful life (RUL) of an engineered system, with an aim to address the aforementioned deficiencies. This paper explores a smart health reasoning system to assess the health condition of power generator stator bars against moisture absorption based on the statistical analysis of the capacitance measurements on bar insulators. In particular, a relative health measure, namely the directional Mahalanobis distance, is proposed to quantify the health condition of a stator bar. The smart health reasoning system is validated using five years' field data from seven generators, each of which contains 42 turns.