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.
Highlights
Asynchronous motors, which have important features such as cost effectiveness, easy speed control, low maintenance cost, and low maintenance requirement, are used in most production lines
An Artificial Neural Network (ANN) is a structure based on a human learning mechanism
The sound signals of the one-phase asynchronous motor were examined through ANNs and the short circuit fault detection in the stator windings were analyzed
Summary
Asynchronous motors, which have important features such as cost effectiveness, easy speed control, low maintenance cost, and low maintenance requirement, are used in most production lines. Detection methods related to asynchronous motor bearing and stator failures were examined [1], [2]. The sound signals of the one-phase asynchronous motor were examined through ANNs and the short circuit fault detection in the stator windings were analyzed. Irudayaraj, Subramani, and Subramaniam [20] proposed a new approach to detect broken rotor bar failures using Hilbert Transform and ANN when the asynchronous three-phase motor is controlled by a motor driver. Liu, Guo, and Wang [21] conducted a study of engine error analysis using the noise-based incomplete wavelet packet analysis - ANN model in gasoline engines When all these studies are examined, it is seen that the usage of ANN has increased in recent years in the detection of motor failures. The explanations on experimental results, intuitiveassisted ANN details, performance, and evaluation, and the results, are presented of the article, respectively
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