Non-contact model-based diagnostics of electrical motor involving uncertainty and imprecision of model parameters

Abstract

System identification of a parametric ''black box" model for the purpose of electrical motor diagnostics is discussed in this paper. The measured acoustic pressure signal is used for identification of a model which structure is considered as a transfer function. Poles of denominator are calculated and collected on a complex plane. Fuzzy, two-stage algorithm is used for clustering and classification of poles which are assumed as symptoms of the motor conditions. The statistical uncertainty and fuzzy imprecision of the poles placement is taken into account by the clasterization procedure. The aim of this procedure is a separation of classes regarding a priori information of their number. Classification was performed with the use of the faulty electrical motors.