  Aimed at the timely detection of the degradation of electrical machines and
the organization of active maintenance, numerous studies on performance
degradation assessment have been conducted. However, previous research still
suffers from two deficiencies: 1) determining the relevant relationship among
diverse machine degradation states and assessing the specific degree of
deterioration and 2) determining the evolutionary relationships among
degradation and failure modes and assessing the failure modes corresponding to
different degradation scenarios. To address these two deficiencies, a novel
performance degradation assessment method is proposed. First, the
self-organizing feature map (SOM) network is used to mine the latent
degradation states of electrical machines. Second, the latent states are
quantified according to established statistical health indexes, and by
analyzing the distribution of extracted health indexes corresponding to
different degradation states, the relevant transition relationships of the
valid degradation states and the final evolving fault types are determined.
Third, a hybrid discrete HMM is developed to fully describe the transition
process among different states and assess the degradation scenario of a machine
in an online manner. The results of a real application of an electric point
machine show that the proposed method can identify valid degradation states and
obtain a superior assessment accuracy.
