Unbalance Detection in Induction Motors through Vibration Signals Using Texture Features

The detection of faults in induction motors has been one of the main challenges to the industry in recent years. An effective fault detection method is fundamental to ensure operational security and productivity. Different models for intelligent fault diagnosis have been recently proposed. However, not all of them are accessible for some manufacturing processes because of the black-box approach, the complexity of hyperparameter tuning, high-dimensionality feature vectors, and the need for sophisticated computational resources. In this paper, a method for the detection of an unbalance fault in induction motors based on a low-dimensional feature vector and a low-complexity classification approach is proposed. The feature vector presented in this manuscript is based on texture features, which are a basic tool for image processing and image understanding. Nevertheless, texture features have not been explored as a powerful instrument for induction motor fault analysis. In this approach, texture features are used to analyze a set of vibration signals belonging to two different classes: an unbalanced motor and a healthy motor. Training-validation and testing stages are developed to build and evaluate the performance of the classifier, respectively. The results show higher accuracy and lower training time in comparison with different state-of-the-art approaches.

Automated summary

The detection of faults in induction motors is a major challenge for the industry. This paper proposes a method for the detection of an unbalance fault in induction motors using a low-dimensional feature vector and a low-complexity classification approach. The method utilizes texture features to analyze vibration signals from unbalanced and healthy motors. Experimental results show higher accuracy and lower training time compared to state-of-the-art approaches.