Fault Diagnosis and Prognosis of a Brushless DC motor using a Model-based Approach

Abstract

This paper proposes a model-based fault diagnosis and prognosis approach applied to brushless DC motors (BLDC). The objective is an early detection of mechanical and electrical faults in BLDC motors operating under a variety of operating conditions. The proposed model-based method is based on the evaluation of a set of residuals that are computed taking into account analytical redundancy relations. Fault diagnosis consist of two steps: First, checking if at least one of the residuals is inconsistent with the normal operation of the system. And, second, evaluating the set of the residuals that are inconsistent to determine which fault is present in the system. Fault prognosis consists of the same two steps but instead of considering current inconsistencies evaluates drift deviations from nominal operation to predict futures residual inconsistencies and therefore predict future fault detections and diagnosis. A description of various kinds of mechanical and electrical faults that can occur in a BLDC motor is presented. The performance of the proposed method is illustrated through simulation experiments.