# Unbalance Detection in Induction Motors through Vibration Signals Using Texture Features
###### Abstract  
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. Keywords:signal processing; intelligent diagnosis; supervised classification; unbalance detection +
Footnote †: journal: Medical  
unbalance faults in IM. In the [PERSON] et al. approach, different kernels are used to compute the convolution and extract features from the short-time Fourier transform matrix set. [PERSON] et al. achieved 99.7% of accuracy in the identification of an unbalance fault. On the other hand, RNNs are models based on internal states that allow the remembrance of the faults of the motors. [PERSON] et al. [15] presented an RNN used to classify the unbalance fault. In the [PERSON] et al. approach, the acceleration signals and a set of 29 statistical features are used to train and test the RNN. [PERSON] et al. achieved a 98.9% of accuracy in the detection of the unbalance fault. Although the deep neural network approaches allow the classification of the unbalance fault in IM, they are complex in terms of hyperparameter tuning. In addition, the training time and the computational resources are often expensive, hindering the implementation of a monitoring system in the industry [16]. Statistical classifiers arise as an alternative to complex black-box models. These approaches are based on statistical measurements that allow the description of a given signal. In the detection of faults in IM, these models have been used in the classification of faults such as broken rotor bars, rotor misalignment, bearing faults, unbalance, and many others [17; 18; 19; 20]. Within the unbalance fault, signal analysis can be performed in both time or frequency domains. In the time domain, [PERSON] et al. [21] extracted the statistical features of kurtosis, impulse factor, skewness, RMS, range, and variance in the time domain to classify the unbalance fault with 86.87% accuracy. The detection of faults using the frequency domain signals can also be achieved using statistical features. [PERSON] et al. [22] proposed a set of statistical features based on the frequency domain and the time domain to infer the unbalance fault. The features vector is composed of standard deviation, skewness, and kurtosis. Using a support vector machine to classify the unbalance fault, [PERSON] et al. [22] achieved 84.51% of accuracy. Although the methods previously mentioned achieved the classification of the unbalanced fault, the use of high-dimensional feature vectors and low representative features makes it difficult to obtain high accuracy in the final model [23]. For these reasons, new features need to be studied to decrease the computational load and keep the accuracy rates high.