Search Results (313)

As power grids scale and aging assets edge toward obsolescence, grounding grid corrosion has become a critical vulnerability. Conventional diagnosis must fit high-dimensional electrical data to a physical model, typically yielding a nonlinear under-determined system fraught with computational burden and uncertainty. We propose the Enhanced Biomimetic Hippopotamus Optimization (EBOHO) algorithm, which distills the river-dwelling hippo’s ecological wisdom into three synergistic strategies: a beta-function herd seeding that replicates the genetic diversity of juvenile hippos diffusing through wetlands, an elite–mean cooperative foraging rule that echoes the way dominant bulls steer the herd toward nutrient-rich pastures, and a lens imaging opposition maneuver inspired by moonlit water reflections that spawn mirror candidates to avert premature convergence. Benchmarks on the CEC 2017 suite and four classical design problems show EBOHO’s superior global search, robustness, and convergence speed over numerous state-of-the-art meta-heuristics, including prior hippo variants. An industrial case study on grounding grid corrosion further confirms that EBOHO swiftly resolves the under-determined equations and pinpoints corrosion sites with high precision, underscoring its promise as a nature-inspired diagnostic engine for aging power system infrastructure. Full article

Resource reutilization of industrial waste mud has encountered challenges due to its high water content, enhanced fluidity, and inherent difficulty in segregating mud and water phases. The author first screened out efficient flocculants through flocculation dehydration tests and then adopted the technology of vacuum filtration combined with electroosmosis dehydration to conduct deep dehydration of waste mud. Among them, the independently designed vacuum filtration electroosmosis system effectively solves the problems of easy clogging and bending of the traditional system. On this basis, geopolymer fluid solidified soil was prepared using dehydrated mud, furnace slag, and fly ash as raw materials, and the influencing factors of its long-term service performance were studied. It was confirmed that the efficient treatment capacity of the combined dehydration technology for industrial waste mud, and the geopolymer fluid solidified soil prepared from dehydrated mud has engineering application potential. This research provides a reference for the resource utilization of industrial waste mud. Full article

Fused Deposition Modeling (FDM) has evolved from a rapid prototyping technique to an established manufacturing process for various industrial applications, including aerospace, robotics, biomedical engineering, and food production. Despite its versatility, the surface quality and dimensional accuracy of FDM-printed parts remain significant challenges, limiting their applicability in high-performance and precision-driven industries. Some of the primary limitations of FDM are volumetric error, shape deviation, and surface roughness, which directly affect the mechanical properties and functional performance of printed components. Post-processing techniques are available to mitigate these problems. Among the available post-processing techniques, CNC machining has emerged as a viable solution for improving the surface finish and dimensional precision of FDM parts. The integration of subtractive CNC machining with additive FDM printing enables the development of hybrid manufacturing strategies, leveraging the design freedom of 3D printing while ensuring superior surface quality. This paper presents a comprehensive review of recent studies on CNC post-processing of FDM-printed parts, analyzing its impact on surface roughness, dimensional accuracy, and material properties. Additionally, key process parameters influencing the effectiveness of CNC machining are discussed. Full article

In the context of the rapid popularization of clean energy, the precise identification of surface defects on photovoltaic modules has become a core technical bottleneck limiting the operational efficiency of power stations. In response to the shortcomings of existing detection methods in identifying tiny defects and model efficiency, this study innovatively constructed the EER-DETR detection framework: firstly, a feature reconstruction module WDBB with a differentiable branch structure was introduced to significantly enhance the feature retention ability for fine cracks and other small targets; secondly, an adaptive feature pyramid network EHFPN was innovatively designed, which achieved efficient integration of multi-level features through a dynamic weight allocation mechanism, reducing the model complexity by 9.7% while maintaining detection accuracy, solving the industry problem of “precision—efficiency imbalance” in traditional feature pyramid networks; finally, an enhanced upsampling component was introduced to effectively address the problem of detail loss that occurs in traditional methods during image resolution enhancement. Experimental verification shows that the improved algorithm increased the average precision (mAP@0.5) on the panel dataset by 1.9%, and its comprehensive performance also exceeded RT-DETR. Based on the industry standard PVEL-AD, the detection rate of typical defects significantly improved compared to the baseline model. The core innovation of this research lies in the combination of differentiable architecture design and dynamic feature management, providing a detection tool for the intelligent operation and maintenance of photovoltaic power stations that possesses both high precision and lightweight characteristics. It has significant engineering application value and academic reference significance. Full article

The widespread scrapping of retired mechanical parts has led to severe waste of resources and environmental burdens, posing a significant challenge to sustainable industrial development. To enable efficient recycling of retired mechanical parts and enhance the sustainability of their remanufacturing processes, the concept of biological genes is adopted to characterize the changes in the information of retired mechanical parts during the remanufacturing process as gene mutations of parts, aiming to maximize remanufacturing potential and devise an optimal generalized remanufacturing strategy for extending part life cycles. However, gene mutation of retired mechanical parts is not an isolated event. The modification of local genes may disrupt the original equilibrium of the part’s state, leading to conflicts such as material–performance, structure–function/performance, and function–performance. These conflicts constitute a major challenge and bottleneck in designing generalized remanufacturing schemes. Therefore, we propose a conflict identification and resolution method for gene mutation of retired mechanical parts. First, gene mutation graph of retired mechanical parts is established to express its all-potential remanufacturing pathways. Using discrimination rules and the element representation method from extenics, mutation conflicts are identified, and a conflict problem model is constructed. Then, the theory of inventive problem solving (TRIZ) engineering parameters are reconstructed and mapped to the mutation conflict parameters. The semantic mapping between the inventive principles and the transforming bridges is established by the Word2Vec algorithm, thereby improving the transforming bridge method to generate conflict resolution solutions. A coexistence degree function of transforming bridges is proposed to verify the feasibility of the resolution solutions. Finally, taking the generalized remanufacturing of a retired gear shaft as an example, we analyze and discuss the process and outcome of resolving gene mutation conflicts, thereby verifying the feasibility and effectiveness of the proposed concepts and methodology. Full article

Robotics has widespread applications throughout industrial automation, autonomous vehicles, agriculture, and more. For these reasons, undergraduate education has begun to focus on preparing engineering students to directly contribute to the design and use of such systems. However, robotics is inherently multi-disciplinary and requires knowledge of controls and automation, embedded systems, sensors, signal processing, algorithms, and artificial intelligence. This makes training the future robotics workforce a challenge. In this paper, we evaluate our experiences with project-based learning approaches to teaching robotics at the undergraduate level at Miami University. Specifically, we analyze three consecutive years of capstone design projects on increasingly complex robotics design problems for multi-robot systems. We also evaluate the laboratories taught in our course “ECE 314: Elements of Robotics”. We have chosen these four experiences since they focus on the use of “cheap” first-principled robots, meaning that these robots sit on the fringe of embedded system design in that much of the student time is spent on working with a micro-controller interfacing with simple and cheap actuators and sensors. To contextualize our results, we propose the Robotic System Levels (RSL) model as a structured way to understand the levels of abstraction in robotic systems. Our main conclusion from these case studies is that, in each experience, students are exposed primarily to a subset of levels in the RSL model. Therefore, the curriculum should be designed to emphasize levels that align with educational objectives and the skills required by local industries. Full article

This study addresses the freeze-up problem in HVAC system heat exchangers of industrial buildings in severely cold regions by proposing a collaborative anti-freeze control strategy based on multi-objective optimization. Taking a diesel engine laboratory as the research case, key freezing-inducing factors were identified through system performance analysis and fault diagnosis. An innovative interlocked anti-freeze control system was developed by integrating electric heating with dynamic regulation of bypass air volume. Utilizing gray relational analysis, the optimal interlock control scheme was selected from four alternatives based on a comprehensive performance evaluation. Multi-objective optimization through the NSGA-II algorithm was performed on parameters including the set temperature, water flow rate, and fresh air volume, achieving coordinated optimization of energy consumption (11.4% reduction compared to pre-optimization) and thermal comfort. TRNSYS-based simulation verification demonstrated that the system maintains a 94.71% freeze protection time assurance rate under extreme operating conditions, effectively resolving the reliability deficiencies of traditional solutions in severely cold environments. This research provides a novel method for industrial building HVAC system anti-freeze design that harmonizes energy efficiency and comfort performance. Full article

The pandemic forced educators to shift abruptly to distance learning, also referred to as e-learning education. Educational institutions integrated new educational tools and online platforms. Several schools, colleges, and universities began incorporating online laboratories in different fields of education, such as engineering, information technology, physics, and chemistry. Online laboratories may take the form of virtual laboratories, software-based simulations available via the Internet, or remote labs, which involve accessing physical equipment online. Adopting remote laboratories as a substitute for conventional hands-on labs has raised concerns regarding the safety and security of both the remote lab stations and the Online Laboratory Management Systems (OLMSs). Design patterns and architectures need to be developed to attain security by design in remote laboratories. Before these can be developed, software architects and developers must understand the domain and existing and proposed solutions. This paper presents an extensive literature review of safety and security concerns related to remote laboratories and an overview of the industry, national and multinational standards, and legal requirements and regulations that need to be considered in building secure and safe Online Laboratory Management Systems. This analysis provides a taxonomy and classification of published standards as well as security and safety problems and possible solutions that can facilitate the documentation of best practices, and implemented solutions to produce security by design for remote laboratories and OLMSs. Full article

Stabilization and tracking problems for cart inverted pendulums under disturbances and uncertainties have posed significant challenges for control engineers. While various controllers have been designed for an inverted pendulum, they often overlook the calibration error of the pendulum angle in practical implementations, which degrades the control performance. Incorrect calibration of the pendulum angle in upright equilibrium position generates an offset of cart position errors. To solve this problem, an augmented model comprising integral cart position errors was first constructed. Afterwards, a sliding mode control was designed for this system based on a linear quadratic controller, to facilitate implementation. Additionally, a stepper motor was employed in the inverted pendulum to enhance the control performance and widen applicability in industrial settings. The effectiveness and performance of the proposed controller were validated by means of experimental studies, focusing on stabilization control and tracking control of a cart inverted pendulum actuated by a stepper motor. Full article

The flow of viscous and highly viscous fluids in straight and bent pipes and channels is a fundamental process in a wide variety of industrial applications and is, therefore, of great interest in science and engineering. Understanding the physics behind such flows has a direct impact on the design of efficient, safe and reliable systems. The type of fluid, which can be viscous or even highly viscous, and the pipe geometry can affect the flow dynamics, the pressure loss and the overall efficiency of the process. In this paper, we provide an extensive review of the state-of-the-art research concerning the flow of Newtonian and non-Newtonian, single-phase fluids in straight and bent pipes. Since a big amount of work in the literature is devoted to the study of Newtonian pipe flows, the paper starts with a brief outline of the nonlinear theory of viscous Newtonian fluid flow in pipes, including a survey of early and recent analytical solutions to the Navier–Stokes equations. The central part of the paper deals with an extensive overview of existing experimental and numerical research work on viscous Newtonian pipe flows. Separate sections are devoted to non-Newtonian fluid flows, the problem of entropy generation due to irreversible processes in the flow and hydromagnetic Newtonian and non-Newtonian pipe flow. The review closes with a brief survey of machine learning and artificial intelligence modeling applied to pipe flow along with future trends and challenges in pipe flow research. Full article

The accelerated growth of scientific publications in renowned databases such as Scopus (Elsevier) and Web of Science (Clarivate) has made the identification of unresolved research problems increasingly complex. This challenge is exacerbated by the vast amount of information that must be analyzed, highlighting the imminent need for the application of big data techniques to extract relevant information for researchers, stakeholders in innovation and development, and regulatory policymakers. To address this challenge, this article presents an innovative, structured, and systematic methodology for conducting bibliometric analyses of scientific publications. The proposed approach is designed for researchers who only have an initial research idea, a broad problem context, or a general study area and require methodological tools to precisely define their research problem. The methodology follows a recommended flowchart-guided process, leveraging open-source tools such as Bibliometrix (R), spreadsheets, and text processing techniques to conduct a comprehensive bibliometric study. This enables the analysis of the intellectual, conceptual, and social structures of a research field, facilitating the identification of research gaps and emerging trends. As a practical application, the proposed methodology was implemented for the 2004–2024 period, within the framework of an applied research project in engineering. This case study aimed to answer key research questions formulated during the study design phase, demonstrating the effectiveness of the approach in systematically analyzing scientific production. Beyond the energy sector and energy systems, this methodology has proven to be adaptable to diverse disciplines, such as health sciences, industrial management, construction, and urban development, provided that relevant databases are accessible. Through this structured approach, researchers can better define their research problems and identify future challenges in various areas of knowledge. Full article

The last decades have offered new challenges to researchers worldwide through the problems our planet is facing both in the environmental protection field and the need to replace fossil fuels with new environmentally friendly alternatives. Bioenergy, as a form of renewable energy, is an acceptable option from all points of view, and biofuels, due to their biological origin, have the ability to satisfy the new needs of humanity. As they release non-polluting combustion products into the atmosphere, biofuels have already been adopted as additives in traditional liquid fuels, intended mainly for the internal combustion engines of automobiles. The current work proposes an extension of the biofuel application in combustion processes specific to industrial furnaces. This technical concern has not been found in the literature, except for the achievements of the research team involved in this work, who performed the previous investigations. A 51.5 kW burner was designed to operate with glycerin originating from the triglycerides of plants and animals, mixed with ethanol, an alcohol produced by the chemical industry recently used as an additive in gasoline for automobile engines. Industrial oxygen was chosen as the oxidizing agent necessary for the liquid mixture combustion, allowing us to obtain much higher flame temperatures compared with the usual combustion processes using air. Mixing glycerin with ethanol in an 8.8 ratio allowed for growing flame stability, also accentuated by creating swirl currents in the flame through the speed regime of fluids at the exit from the burner body. Results were excellent in both the flame stability and low level of polluting emissions. Full article

The civil engineering industry faces a critical need for innovative non-destructive evaluation methods, particularly for ageing critical infrastructure, such as bridges, where current techniques fall short. Muography, a non-invasive imaging technique, constructs three-dimensional density maps by detecting the interactions of naturally occurring cosmic-ray muons within the scanned volume. Cosmic-ray muons offer both deep penetration capabilities due to their high momenta and inherent safety due to their natural source. However, the technology’s reliance on this natural source results in a constrained muon flux, leading to prolonged acquisition times, noisy reconstructions, and challenges in image interpretation. To address these limitations, we developed a two-model deep learning approach. First, we employed a conditional Wasserstein Generative Adversarial Network with Gradient Penalty (cWGAN-GP) to perform predictive upsampling of undersampled muography images. Using the Structural Similarity Index Measure (SSIM), 1-day sampled images were able to match the perceptual qualities of a 21-day image, while the Peak Signal-to-Noise Ratio (PSNR) indicated a noise improvement to that of 31 days worth of sampling. A second cWGAN-GP model, trained for semantic segmentation, was developed to quantitatively assess the upsampling model’s impact on each of the features within the concrete samples. This model was able to achieve segmentation of rebar grids and tendon ducts embedded in the concrete, with respective Dice–Sørensen accuracy coefficients of 0.8174 and 0.8663. This model also revealed an unexpected capability to mitigate—and in some cases entirely remove—z-plane smearing artifacts caused by the muography’s inherent inverse imaging problem. Both models were trained on a comprehensive dataset generated through Geant4 Monte Carlo simulations designed to reflect realistic civil infrastructure scenarios. Our results demonstrate significant improvements in both acquisition speed and image quality, marking a substantial step toward making muography more practical for reinforced concrete infrastructure monitoring applications. Full article

Nowadays, induction motors are an essential part of industrial development. Faults due to short-circuit turns within induction motors are “incipient faults”. This type of failure affects engine operation through undesirable vibrations. Such vibrations negatively affect the operation of the system or the products with which said motor is in contact. Early fault detection prevents sudden downtime in the industry that can result in heavy economic losses. The incipient failures these motors can present have been a vast research topic worldwide. Existing methodologies for detecting incipient faults in alternating current motors have the problem that they are implemented at the simulation level, or are invasive, or do not allow in situ measurements, or their digital implementation is complex. This article presents the design and development of a purpose-specific system capable of detecting short-circuit faults in the turns of the induction motor winding without interrupting the motor’s working conditions, allowing online measurements. This system is standalone, portable and allows non-invasive and in situ measurements to obtain phase currents. These data form classified descriptors using a multilayer perceptron neural network. This type of neural network enables agile and efficient digital processing. The developed neural network could classify current faults with an accuracy rate of 93.18%. The neural network was successfully implemented on a low-cost and low-range purpose-specific Field Programmable Gate Array board for online processing, taking advantage of its computing power and real time processing features. The measurement of phase current and the class of fault detected is displayed on a liquid-crystal display screen, allowing the user to take necessary actions before major faults occur. Full article

With the continuous development of industrial automation, diesel engines play an increasingly important role in various types of construction machinery and power generation equipment. Improving the dynamic and static performance of the speed control system of single-cylinder diesel engines can not only significantly improve the efficiency of the equipment, but also effectively reduce energy consumption and emissions. Particle swarm optimization (PSO) fuzzy PID control algorithms have been widely used in many complex engineering problems due to their powerful global optimization capability and excellent adaptability. Currently, PSO-based fuzzy PID control research mainly integrates hybrid algorithmic strategies to avoid the local optimum problem, and lacks optimization of the dynamic noise suppression of the input error and the rate of change of the error. This makes the algorithm susceptible to the coupling of the system uncertainty and measurement disturbances during the parameter optimization process, leading to performance degradation. For this reason, this study proposes a new framework based on the synergistic optimization of the untraceable Kalman filter (UKF) and PSO fuzzy PID control for the speed control system of a single-cylinder diesel engine. A PSO-optimized fuzzy PID controller is designed by obtaining accurate speed estimation data using the UKF. The PSO is capable of quickly adjusting the fuzzy PID parameters so as to effectively alleviate the nonlinearity and uncertainty problems during the operation of diesel engines. By establishing a Matlab/Simulink simulation model, the diesel engine speed step response experiments (i.e., startup experiments) and load mutation experiments were carried out, and the measurement noise and process noise were imposed. The simulation results show that the optimized diesel engine speed control system is able to reduce the overshoot by 76%, shorten the regulation time by 58%, and improve the noise reduction by 25% compared with the conventional PID control. Compared with the PSO fuzzy PID control algorithm without UKF noise reduction, the optimized scheme reduces the overshoot by 20%, shortens the regulation time by 48%, and improves the noise reduction effect by 23%. The results show that the PSO fuzzy PID control method with integrated UKF has superior control performance in terms of system stability and accuracy. The algorithm significantly improves the responsiveness and stability of diesel engine speed, achieves better control effect in the optimization of diesel engine speed control, and provides a useful reference for the optimization of other diesel engine control systems. In addition, this study establishes the GT-POWER model of a 168 F single-cylinder diesel engine, and compares the cylinder pressure and fuel consumption under four operating conditions through bench tests to ensure the physical reasonableness of the kinetic input parameters and avoid algorithmic optimization on the distorted front-end model. Full article