Abstract:
The asynchronous machine is the most used in industry due to its robustness and low purchase or
maintenance cost, but it can be exposed to many electrical or mechanical faults during its operation,
which requires early detection. This has led to the use of many diagnostic methods that allow us to
identify and classify faults that occur in the machine, among these techniques is the use of artificial
intelligence and artificial neural networks.
The objective of this work is to diagnose malfunctions of squirrel cage induction machines (broken
bar fault) using artificial neural network techniques. We developed a neural network model to detect
and classify defects, then we performed tests to validate the neural network model
