Image retrieval is a procedure of finding appropriate images in the image database. There are two types of image retrieval systems in common practice. These are the text-based image retrieval (TBIR) system and content-based image retrieval (CBIR) system. The content based system is proven to be more effective in which the visual contents of the images are extracted and described by multi-dimensional feature vectors. In this work, several models are developed by combining different image features in a combination of two and three. To begin with, three different models based on the combination of two features, viz., color with shape, shape with texture, and color with texture are designed. A three features based model is considered with color, shape, and texture in the next step. The retrieval rate of the mentioned models is assessed in terms of precisions. The results are obtained using COREL standard database. This study shows that the images can be better retrieved using three features based model in contrast to models using two features.

Rolling element bearings (REBs) are vital parts of rotating machinery across various industries. For preventing breakdowns and damages during operation, it is crucial to establish appropriate techniques for condition monitoring and fault diagnostics of these bearings. The development of machine learning (ML) brings a new way of diagnosing the fault of rolling element bearings. In the current work, ML models, namely, Support Vector Machine (SVM) and K-Nearest Neighbor (KNN), are used to classify the faults associated with different ball bearing elements. Using open-source Case Western Reserve University (CWRU) bearing data, machine learning classifiers are trained with extracted time-domain and frequency-domain features. The results show that frequency-domain features are more convincing for the training of ML models, and the KNN classifier has a high level of accuracy compared to SVM.

Rolling element bearings are crucial components of rotating machinery used in various industries, including aerospace, navigation, machine tools, etc. Therefore, it is essential to establish suitable techniques for condition monitoring and fault diagnosis of bearings to avoid breakdowns and damages during operation for overall industrial sustainability. Vibration-based condition monitoring has been the most employed technique in this perspective. Many researchers have investigated the vibration response of rolling element bearings having inner race defects, outer race defects, or rolling element defects using conventional techniques in past decades. However, Machine Learning (ML) has emerged as another way of bearing fault diagnosis. In this work, fault signatures of ball bearings are classified using a total of 6 (with 24 subcategories) ML models, and a comparative performance of these models is presented. The ML classifiers are trained with extracted time-domain and frequency-domain features using open-source Case Western Reserve University (CWRU) bearing data. Two datasets of different sample size and number of samples of vibration data corresponding to a healthy ball bearing, a defective bearing with inner race defect, a ball defect, and an outer race defect, running at a particular set of working conditions, are considered. The accuracy of ML models is compared to identify the best model for classifying the faults under consideration.

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