Artificial Bee and Ant Colony-assisted Performance Improvements in Artificial Neural Network-based Rotor Fault Detection

Abstract

Asynchronous motors are the most commonly used types of motor in the industry. They are preferred because of their ease of control and reasonable cost. Since it is not desirable to suspend production in factories, it is required that motor failures used in production lines be detected quickly and easily. In this article, sound signals were recorded during the operation of the asynchronous motor, which is operational and with a rotor bar crack; and filtering, normalization, and Fast Fourier Transform were performed. The detection of rotor broken bar error was examined using the feed-forward backpropagation Artificial Neural Network (ANN) method. With intuitive algorithms such as the artificial bee colony and artificial ant colony, improvements to the ANN results were investigated. The experimental results verified that intuitive algorithms can improve the estimation performance of the neural network.