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Implementing condition-based maintenance using modeling and simulation: a case study of a permanent magnet synchronous motor

 This paper introduces condition-based maintenance (CBM) architecture regarding an electrical application. Appropriate and efficient fault detection constitutes one of the major challenges associated with CBM and a model-based approach constitutes the way to achieve it. A case study using a permanen...

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Main Authors: Quiroga Méndez, Jabid Eduardo, Oviedo Castillo, Silvia
Format: Article
Language:English
Published: 2011
Subjects:
condition-based maintenance
fault detection
neuronal networks
permanent magnet synchronous motor
Ingeniería Mecánica - Mecatronica
detección de fallas
mantenimiento basado en la condición
motor sincrónico de imanes permanentes
redes neuronales
Online Access:https://dialnet.unirioja.es/servlet/oaiart?codigo=4902326
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Summary: This paper introduces condition-based maintenance (CBM) architecture regarding an electrical application. Appropriate and efficient fault detection constitutes one of the major challenges associated with CBM and a model-based approach constitutes the way to achieve it. A case study using a permanent magnet synchronous motor (PMSM) is presented to illustrate implementing CBM using a neural network motor model. CBM may be implemented in real time using Matlab and dSpace. The difference between line currents' negative sequence components, predicted by a multilayer neural network, and the current values acquired from the motor is used as fault indicator. Experimental results have shown the efficiency of the proposed model in detecting several stator winding short faults in differing load conditions and fault severity, obtaining up to 95% reliability. 

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