Nowadays detection of deterioration of electrical motors is an important topic of research. Vibration signals often carry diagnostic information of a motor. The authors proposed a setup for the analysis of vibration signals of three-phase induction motors. In this paper rotor fault diagnostic techniques of a three-phase induction motor (TPIM) were presented. The presented techniques used vibration signals and signal processing methods. The authors analyzed the recognition rate of vibration signal readings for 3 states of the TPIM: healthy TPIM, TPIM with 1 broken bar, and TPIM with 2 broken bars. In this paper the authors described a method of the feature extraction of vibration signals Method of Selection of Amplitudes of Frequencies – MSAF-12. Feature vectors were obtained using FFT, MSAF-12, and mean of vector sum. Three methods of classification were used: Nearest Neighbor (NN), Linear Discriminant Analysis (LDA), and Linear Support Vector Machine (LSVM). The obtained results of analyzed classifiers were in the range of 97.61 % – 100 %.

This study offers a complete analysis of the use of deep learning or machine learning, as well as precise recommendations on how these methods could be used in the creation of machine components and nodes. The examples in this thesis are intended to identify areas in mechanical design and optimization where this technique could be widely applied in the future, benefiting society and advancing the current state of modern mechanical engineering. The review begins with a discussion on the workings of artificial intelligence, machine learning, and deep learning. Different techniques, classifications, and even comparisons of each method are described in detail. The most common programming languages, frameworks, and software used in mechanical engineering for this problem are gradually introduced. Input data formats and the most common datasets that are suitable for the field of machine learning in mechanical design and optimization are also discussed. The second half of the review describes the current use of machine learning in several areas of mechanical design and optimization, using specific examples that have been investigated by researchers from around the world. Further research directions on the use of machine learning and neural networks in the fields of mechanical design and optimization are discussed.

Natural disasters not only disturb the human ecological system but also destroy the properties and critical infrastructures of human societies and even lead to permanent change in the ecosystem. Disaster can be caused by naturally occurring events such as earthquakes, cyclones, floods, and wildfires. Many deep learning techniques have been applied by various researchers to detect and classify natural disasters to overcome losses in ecosystems, but detection of natural disasters still faces issues due to the complex and imbalanced structures of images. To tackle this problem, we propose a multilayered deep convolutional neural network. The proposed model works in two blocks: Block-I convolutional neural network (B-I CNN), for detection and occurrence of disasters, and Block-II convolutional neural network (B-II CNN), for classification of natural disaster intensity types with different filters and parameters. The model is tested on 4428 natural images and performance is calculated and expressed as different statistical values: sensitivity (SE), 97.54%; specificity (SP), 98.22%; accuracy rate (AR), 99.92%; precision (PRE), 97.79%; and F1-score (F1), 97.97%. The overall accuracy for the whole model is 99.92%, which is competitive and comparable with state-of-the-art algorithms.

This article presents a model of the geometry of teeth profiles based on the path of contact definition. The basic principles of the involute and convex–concave teeth profile generation are described. Due to the more difficult manufacturing of the convex–concave gear profile in comparison to the involute one, an application example was defined that suppressed this disadvantage, namely a planetary gearbox with plastic-injection-molded gears commonly used in vehicle back-view mirror positioners. The contact pressures and the slide ratios of the sun, planet, and ring gears with both teeth profile variants were observed and the differences between the calculated parameters are discussed.

Diabetic retinopathy (DR) is a diabetes disorder that disturbs human vision. It starts due to the damage in the light-sensitive tissues of blood vessels at the retina. In the beginning, DR may show no symptoms or only slight vision issues, but in the long run, it could be a permanent source of impaired vision, simply known as blindness in the advanced as well as in developing nations. This could be prevented if DR is identified early enough, but it can be challenging as we know the disease frequently shows rare signs until it is too late to deliver an effective cure. In our work, we recommend a framework for severity grading and early DR detection through hybrid deep learning Inception-ResNet architecture with smart data preprocessing. Our proposed method is composed of three steps. Firstly, the retinal images are preprocessed with the help of augmentation and intensity normalization. Secondly, the preprocessed images are given to the hybrid Inception-ResNet architecture to extract the vector image features for the categorization of different stages. Lastly, to identify DR and decide its stage (e.g., mild DR, moderate DR, severe DR, or proliferative DR), a classification step is used. The studies and trials have to reveal suitable outcomes when equated with some other previously deployed approaches. However, there are specific constraints in our study that are also discussed and we suggest methods to enhance further research in this field.

Erosive wear due to the fact of sand severely affects hydrocarbon production industries and, consequently, various sectors of the mineral processing industry. In this study, the effect of the elbow geometrical configuration on the erosive wear of carbon steel for silt–water–air flow conditions were investigated using material loss analysis, surface roughness analysis, and microscopic imaging technique. Experiments were performed under the plug flow conditions in a closed flow loop at standard atmospheric pressure. Water and air plug flow and the disperse phase was silt (silica sand) with a 2.5 wt % concentration, and a silt grain size of 70 µm was used for performing the tests. The experimental analysis showed that silt impact increases material disintegration up to 1.8 times with a change in the elbow configuration from 60° to 90° in plug flow conditions. The primary erosive wear mechanisms of the internal elbow surface were sliding, cutting, and pit propagation. The maximum silt particle impaction was located at the outer curvature in the 50° position in 60° elbows and the 80° position in 90° elbows in plug flow. The erosion rate decreased from 10.23 to 5.67 mm/year with a change in the elbow angle from 90° to 60°. Moreover, the microhardness on the Vickers scale increased from 168 to 199 in the 90° elbow and from 168 to 184 in the 60° elbow.