Condition-based predictive maintenance using neural networks

Rafael De La Fuente, University of Texas at El Paso


In this study, a motor condition diagnostic was achieved through the implementation of an Artificial Neural Network, successfully applying neural network into a predictive maintenance system. Electrical DC motors were monitored to obtain data to train the ANN. Out of these monitoring, vibration signatures were used as the input layer, and the motor condition was used as the expert training information. The main objectives were to apply neural networks to a condition based predictive maintenance, analyze how different neural configurations respond to the problem exposed and analyze pros and cons of the use of neural networks in this field. ^

Subject Area

Engineering, Mechanical|Artificial Intelligence|Computer Science

Recommended Citation

De La Fuente, Rafael, "Condition-based predictive maintenance using neural networks" (2003). ETD Collection for University of Texas, El Paso. AAIEP10535.