Journal of Mechanical Engineering, Automation and Control Systems
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Advancing industrial gas turbine field performance testing: a review of procedures and key considerations with emerging technologies
Get PDFThis review explores the possibility of enhancing the efficiency and accuracy of Industrial Gas turbine Performance testing by critically assessing the traditional methods, their limitations, and how modern technologies can be used to complement the existing traditional testing approaches, optimize data acquisition, and predict operational failures. A systematic and comprehensive search strategy was employed to identify relevant academic and industry literature. Studies on traditional testing practices were reviewed to highlight their constraints, while researches involving the application of emerging technologies for performance diagnostics were also reviewed to illustrate their benefits. Findings show that measured data such as turbine inlet temperature, compressor pressure ratio, exhaust temperature, fuel flow, shaft speed, and vibration remain essential for both traditional and AI-enhanced methods. These parameters, typically obtained through standardized testing procedures, provide the foundational input for AI models such as machine learning algorithms and digital twins. The study revealed that AI technologies thrive in data-rich, repeatable environments by enhancing processes like instrumentation, data logging, and normalization. The study also revealed that machine learning, deep learning, artificial neural networks, and digital twins can be used for more effective planning, reduce redundant testing, and mitigate delays caused by variable factors like weather or load conditions
Advancing product and process innovation through knowledge-sharing networks among European industrial SMEs
Get PDFA methodical approach is created in this study to aid SMEs across Europe (Turkiye, UK, Belgium, Italy) in their product and process development endeavors. Methods and processes that must be followed are examined and streamlined, starting from the point of client contact, and ending with establishment of a pilot production line. This study provides strategies to help technicians and engineers create excellent product designs. Though most of the concepts and methods it will produce are anticipated to be applicable to the design of all types of goods, its primary focus is on the engineering-related aspects of product design. The formulation of problems and the conceptual and embodiment phases of design are the main topics of this work. It will support designers in SMEs with problem identification, explanation, and generation and assessment of solutions. To understand their approach and issues, a thorough search in this field is conducted, along with interviews with multiple SMEs. Additionally, to ascertain the SMEs’ network architecture and how their technical employees interact inside these organizations in a way that supports the creation of new products. This will demonstrate these SMEs' advantages or strengthen their creative positions in the face of global competition
Multi-parameter inversion of concrete face rockfill dam using wild horse optimizer and optimal polynomial chaos kriging
Get PDFStructural parameter inversion is essential for monitoring and assessing the risks of concrete face rockfill dams. Current parameter inversion techniques are, however, often overly complex, computationally demanding, and inefficient, especially when the dam is simulated with a 3D nonlinear finite element method. This study proposes a novel approach combining the Wild Horse Optimizer with Optimal Polynomial Chaos Kriging (WHO_OPCK) to tackle these issues. The method benefits from the low computational cost of optimal polynomial chaos kriging and the fast convergence of the wild horse optimizer. By incorporating statistical uncertainty in input parameters, the method successfully inverts four key constitutive parameters φ, Kb, K, and Rf based on displacement data from a complex dam. The approach proves practical and cost-effective in real engineering applications and has culminated in the development of specialized software that streamlines this structural parameter inversion process. Sensitivity analysis using Sobol’ indices further highlights the importance of each parameter at a low computational cost. The study highlights two key advantages of WHO_OPCK: (i) Unlike traditional methods that struggle with complex dams, WHO_OPCK significantly reduces computational costs and handles parameter determination efficiently. (ii) Compared to other surrogate model combinations with WHO, the proposed WHO_OPCK method offers superior accuracy and efficiency. This method establishes a solid foundation for multi-parameter inversion in concrete face rockfill dams
Multi source heterogeneous data diagnosis method of rotating machinery based on parameter collaborative optimization of multi-scale convolutional autoencoder
Get PDFIn order to fully utilize the features of multi-source heterogeneous data and effectively improve the accuracy and efficiency of fault diagnosis of rotating machinery, a multi-source heterogeneous data diagnosis method based on parameter collaborative optimization multi-scale convolutional autoencoder (MSCAE) is proposed. Firstly, multi-scale information learning is integrated into the convolutional autoencoder (CAE) to consider the temporal and spatial feature information of the diagnostic object simultaneously. To improve the training and diagnostic efficiency of MSCAE, a quantum particle swarm optimization (QPSO) module is used to perform hyperparameter optimization on it using chaos initialization and dynamic weight strategy (DWS). Besides, the sparse attention mechanism is introduced into the MSCAE model to improve the recognition rate of key fault features hidden in the original heterogeneous signals. Finally, the confusion matrix and visualization techniques are used to achieve fault classification. The experimental results demonstrate that after 100 experiments, the proposed method has an average diagnostic accuracy of 98.5 % and strong robustness to noise, providing a new method for rotating machinery fault diagnosis based on multi-source heterogeneous data
A multi-scale convolutional Siamese network for few-shot fault diagnosis of unmanned aerial vehicle rotor
Get PDFUnmanned Aerial Vehicles (UAVs) often face infrequent fault occurrences and high manual annotation costs, resulting in a critical shortage of valid fault samples for diagnostic research. Traditional fault diagnosis methods struggle with small sample sizes. This paper proposes a novel deep metric learning method, the Multi-Scale Convolutional Siamese Network (MSCSN), to address the few-shot learning problem in UAV rotor fault diagnosis. First, discrete wavelet transform (DWT) is used to compress and normalize the vibration signals, enhancing the prominence of signal features. Then, based on the multi-scale convolutional neural network (MS-CNN) model, the network automatically extracts multi-level features from rotor fault vibration signals, improving its adaptability to complex data. Finally, the Siamese network structure, with shared parameters and identical architecture, processes sample pairs and incorporates a small number of support samples for few-shot learning. Experimental results show that the proposed model achieves a highest accuracy of 94.42 % in few-shot tasks. In cross-domain transfer learning tests, the model achieves an average accuracy of 90.28 %, demonstrating its superior generalization ability and robustness across different environments. We also validated the model's stability using the publicly available MVS-UAV-BF dataset
Analysis of the natural characteristics of fiber-reinforced cantilever beams using 8-node solid elements
Get PDFA combined theoretical and experimental approach is employed to investigate the dynamic characteristics of fiber-reinforced cantilever beams. An 8-node element method establishes the theoretical model of the cantilever beam, allowing for the determination of its dynamic properties. A relevant experimental platform is constructed to test the fiber-reinforced cantilever beams, thereby validating the accuracy of the theoretical model. The results indicate that the theoretical model accurately predicts the dynamic characteristics of fiber-reinforced cantilever beams. Finally, based on the established theoretical model, the effects of cantilever beam length, width, and elastic modulus on the dynamic characteristics of the cantilever beam are discussed
Research progress on 3D printed geopolymer materials
Get PDFThe integration of 3D printing technology with geopolymer materials offers a sustainable alternative to conventional construction methods, significantly reducing CO2 emissions. However, challenges such as rapid setting, limited workability, and weak interlayer bonding limit their broader application. This review summarizes recent progress in 3D printed geopolymer composites, focusing on materials selection, rheological optimization, buildability, and mechanical performance enhancement. Strategies including the use of rheology modifiers, fiber reinforcements, nano-additives, and process optimization have shown promise in improving printability and structural performance. Remaining challenges, such as balancing setting time and printability and enhancing interlayer adhesion, are also discussed. Future research directions are proposed to further advance the development of high-performance, low-carbon geopolymer 3D printing materials for sustainable construction
Scientific and practical substantiation of transient processes in asynchronous electric motors of mainline electric locomotives
Get PDFThe research work focuses on scientifically substantiating the operating conditions of small and medium-power auxiliary asynchronous electric motors used in mainline electric locomotives under JSC “Uzbekistan Railways”. The aim is to provide a scientific basis for the operational efficiency of auxiliary asynchronous electric motors and, based on the research findings, to conduct a practical investigation of their service life. This, in turn, will enable timely maintenance of auxiliary asynchronous electric motors in locomotives. Additionally, it will contribute to improving the performance indicators of auxiliary asynchronous electric motors
Analysis of a 10 kW mini pumped hydro storage plant with solar integration in Uzbekistan
Get PDFThis paper presents the design and performance evaluation of a 10 kW mini pumped hydro storage (PSH) system integrated with solar photovoltaic (PV) energy for rural electrification in Uzbekistan. The system stores excess solar energy during the day and generates 60 kWh electricity during evening hours at a rated power of 10 kW, with an overall efficiency of about 75 %. The optimized design includes a Cross-Flow turbine (200 mm diameter, 600 rpm), a 10 m head, and 58 solar panels of 400 W. The study demonstrates that such small PSH systems can provide a cost-effective, long-lifetime alternative to chemical batteries in rural power applications
About long-term stability of functional treatment
Get PDFRelapse has always been the main problem in orthodontics. But is it due to the treatment method? Or the age of the patient or the anatomy of the skull? At the examples of some case histories, these questions are considered and hopefully, will contribute a bit to this eternally controversial subject