The development of diagnostic systems for rotating machines such as induction motors (IMs) is a task of utmost importance for the industrial sector. Reliable diagnostic systems allow for the accurate detection of different faults. Different methods based on the acquisition of thermal images (TIs) have emerged as diagnosis systems for the detection of IM faults to prevent the further generation of faults. However, these methods are based on artisanal feature selection, so obtaining high accuracy rates is usually challenging. For this reason, in this work, a new system for fault detection in IMs based on convolutional neural networks (CNNs) and thermal images (TIs) is presented. The system is based on the training of a CNN using TIs to select and extract the most salient features of each fault present in the IM. Subsequently, a classifier based on a decision tree (DT) algorithm is trained using the features learned by the CNN to infer the motor conditions. The results of this methodology show an improvement in the accuracy, precision, recall, and F1-score metrics for 11 different conditions.

Patterned fabrics may be regarded as periodic textures, which are defined as the regular tessellation of a primitive unit. A patterned fabric is considered as defective when a primitive unit is different from the others. In this paper, we propose a one-class classifier that uses Reduced Coordinated Cluster Representation (RCCR) as features. In the training step, the size of the primitive unit of defect-free fabrics is automatically estimated using a texture periodicity algorithm. After that, the fabrics are split into samples of one unit and their local structure is learnt with the RCCR features in a one-class classifier. During the test step, defective and non-defective fabrics are also split into samples and are analyzed unit by unit. If the features of a given unit do not satisfy the classification criterion, it is considered to be a defect. Among the advantages of the RCCR is that it represents structural information of textures in a low-dimensional feature space with high discrimination performance. Results from experiments on an extensive database of real fabric images show that our method yields accurate detections, outperforming other state-of-the-art algorithms.

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.

In this paper, we propose an approach for salient pixel detection using a rule-based system. In our proposal, rules are automatically learned by combining four saliency models. The learned rules are utilized for the detection of pixels of the salient object in a visual scene. The proposed methodology consists of two main stages. Firstly, in the training stage, the knowledge extracted from outputs of four state-of-the-art saliency models is used to induce an ensemble of rough-set-based rules. Secondly, the induced rules are utilized by our system to determine, in a binary manner, the pixels corresponding to the salient object within a scene. Being independent of any threshold value, such a method eliminates any midway uncertainty and exempts us from performing a post-processing step as is required in most approaches to saliency detection. The experimental results on three datasets show that our method obtains stable and better results than state-of-the-art models. Moreover, it can be used as a pre-processing stage in computer vision-based applications in diverse areas such as robotics, image segmentation, marketing, and image compression.