Asit Apornsupavit, Tossaporn Sirampuj, C. Sopitviriyaporn
et al.
This manuscript presents the development and deployment of a Machinery Generative Artificial Intelligence (AI) Consultant for gas turbine engines in offshore operations. The initiative aims to reduce Mean Time to Repair (MTTR) and Lost Production Opportunities (LPOs) by providing intelligent, real-time support to field technicians. The solution addresses persistent challenges in offshore troubleshooting, where engineers must navigate extensive technical documentation, hundreds of engineering drawings, and complex alarm tags. Delays in contacting Original Equipment Manufacturer (OEM) and the unavailability of machinery engineers around the clock further compound the issue, often resulting in extended downtime and operational inefficiencies. The AI consultant enhances troubleshooting by interpreting first-out alarm messages and recommending corrective actions based on Integrated Equipment Monitoring (IEM) data. It links directly to relevant engineering drawings and integrates with real-time dashboards to provide contextual alerts. The system is designed to understand and respond to HMI shutdown tags and abnormal machinery conditions, offering step-by-step guidance to technicians. This approach streamlines the diagnostic process and reduces reliance on manual document review or external consultation. The Machinery Generative AI Consultant has been successfully deployed in offshore environments, demonstrating measurable reductions in MTTR and unplanned downtime. Field case studies include resolution of Ignition Failures, Anti-Surge Valve Travel Failures, and Lube Oil High Temperature. The AI-enhanced approach has improved first-response accuracy, reduced troubleshooting time, and enabled safer, more efficient operations. Feedback from field users highlights the tool's ability to deliver timely, relevant insights without the need for extensive document searches or delayed OEM support. This work introduces a hybrid approach that combines structured troubleshooting logic with generative AI-driven decision support. It is among the first implementations in the region to integrate HMI and IEM data into an AI-powered tool specifically for gas turbine engines. The solution exemplifies how digital transformation and AI can be applied to legacy equipment to unlock operational value in offshore oil and gas settings. By automating complex diagnostic workflows and embedding expert knowledge into an accessible platform, the AI consultant represents a significant advancement in machinery support and reliability engineering.
Roland Wölfle, Irina Saur‐Amaral, Leonor C. Teixeira
The development of personalized drugs introduces new uncertainties and risks in production machinery design, which can be mitigated through structured workflows. As the commonly used V-Model approach has limitations in dealing with complex multi-domain problems, it is essential to address traceability and relationships between requirements and solutions in a regulated environment to ensure product quality. This study focuses on the conceptual design phase and develops a design methodology called the Case-based Axiomatic Design Assistant (CADA) to address this type of problem. It takes, as a starting point, Axiomatic Design (AD), due to its simplicity and graphical tools for quality evaluation, and Case-Based Reasoning (CBR), due to its capacity to integrate data structures and continuously improve. This combination is put into practice through a visual assistant that utilizes a knowledge graph to represent design elements comprehensively. This article describes the development, implementation, and testing process of CADA, which includes examples of the conceptual design for pharmaceutical manufacturing. The proposed CADA method facilitates systematic requirements analysis, structured reasoning, and solution evaluation, and overcomes the limitations of previous methodologies. It represents a novel approach with an intuitive workflow and advanced graphical capabilities, exemplified in the context of a conceptual design for pharmaceutical manufacturing. The inclusion of intrinsic data labeling capabilities and inference visualization enhances its relevance.
To solve the unrelated parallel batch processing machine scheduling problem (UPBPMSP) with dynamic job arrivals, heterogeneous processing times, and machine heterogeneity, this paper presents an improved artificial bee colony (IABC) algorithm aimed at minimizing the makespan. Three improvements include the following: (1) a hybrid encoding scheme that combines machine allocation coefficients and priority weights, allowing for flexible consideration of machine capabilities and dynamic job priorities; (2) a dual-mode variable neighborhood search strategy to optimize machine allocation and job sequencing simultaneously; (3) a dynamic weight tournament selection mechanism to enhance population diversity and avoid premature convergence. Experimental results show that IABC reduces the makespan by 5% to 25% compared to traditional ABC and genetic algorithms (GAs), with the most significant advantages observed in concentrated job arrival scenarios. Statistical tests confirm that the improvements are statistically significant, validating the effectiveness of the proposed algorithm.
The concept of life skills is related to the way of life that emphasises the mutual exchange of knowledge, attitudes, and interpersonal skills in education. Its objective is to develop diverse skills among students and prepare them to face life’s challenges with determination. The World Health Organization has defined life skills as “the positive behaviours and tendencies that enable a person to adapt in day-to-day life.” Life skills are the abilities that enable a person to adapt and exhibit positive behaviour, allowing them to deal effectively with the problems and challenges of daily life. Life is a unique gift. Therefore, by equipping life with various skills, happiness, peace, and prosperity are created. In this research, with the objectives of the study in mind, an analytical examination of life skills among secondary-level students has been conducted. This research study examines the effects of living conditions, gender, and social class on students’ life skills and presents the findings. Future researchers can build upon this, and other factors affecting the research can also be explored.
Benjamín Castro-Cancino, Waldo Pérez-Martínez, Paulina Vidal-Páez
et al.
In the mountainous and foothill areas of Santiago, Chile, debris flows and debris floods have been recurrent over recent decades, triggered by short-duration, high-intensity summer rainfall events. These events have caused significant damage to infrastructure and have affected the population, including loss of human lives. This study assesses the susceptibility to debris flow and debris flood generation in the Arrayán and Gualtatas stream basins, located in the Metropolitan Region, using satellite and cartographic data. A Susceptibility Index (SI) was determined through the analysis of 14 conditioning factors, grouped into three main categories: geology, geomorphology, and soil conditions. The weighting and ranking of each factor’s importance were carried out using the Analytic Hierarchy Process (AHP). The results, presented in a susceptibility map, indicate that 60.78% of the study area exhibits low to very low susceptibility, 24.64% moderate susceptibility, and 14.58% high to very high susceptibility, concentrated in stream headwaters, steep slopes, and areas with unconsolidated deposits. Recent debris flow events that have reached urban areas coincide with high-susceptibility zones, validating the methodology and cartographic products, which can support land-use planning and risk management efforts.
One of the main reasons for the sharp reduction in the machine and tractor fleet is a decrease in the level of technical service of equipment. The existing equipment does not undergo the types of maintenance provided for it within the time limits established by the regulations. The equipment, devices and tools recommended for carrying out the appropriate types of maintenance are not fully used, or do not meet the modern requirements for routine maintenance for high-powered agricultural machinery. (Research purpose) The research purpose is determining the possibilities of optimizing routine maintenance of agricultural machinery using an intelligent system of measuring devices, instruments and equipment. (Materials and methods) It was determined that the developed system of measuring devices, instruments and equipment represents an integrated approach to the selection and implementation of monitoring and diagnostics of the technical condition of agricultural machinery during all types of its maintenance. The basic principles of the developed system include: reliability (resistance to external influences); unification during maintenance work; modularity during modernization and expansion of functions; automation of individual operations (minimization of manual labor); economic feasibility. (Results and discussion) It has been shown that the research results presented in the paper in the form of recommendations and methodological approaches provide for the widespread introduction the advanced technological and modern means of technical equipment of service enterprises of the agro-industrial complex in the form of measuring devices, instruments and equipment. (Conclusions) The fulfillment of all requirements during the maintenance of agricultural machinery at engineering service enterprises using an intelligent system of measuring devices of instruments and equipment will reduce the downtime of machinery by 1.5-2 times.
This work aims at discussing the implementation of a design project to promote the teaching-learning process of kinematics and dynamics of machinery to early undergraduate students as part of the engineering curriculum. For that, the ping-pong ball launcher challenge is chosen as a practical example, in which students, organized in groups of five, must design and develop a mechanical system capable of launching ping-pong balls. In order to make the design project interesting and exciting for students, the manufacturing and construction of physical prototype is also required. The solutions developed by each group of students must incorporate a planar mechanism and should be able to throw a ping-pong ball into a very precise target. For economic and safety reasons, the mechanical systems designed can only be actuated by gravity. There are no specific theoretical pre-requisites for this project. The fundamental topics addressed in this pedagogical activity are kinematics, dynamics, analysis and synthesis of mechanisms, selection of mechanical components and basic manufacturing and assembly of machines. The implementation of this design project embraces several different pedagogical dimensions, such as lectures, laboratory activities, deliverables, written reports, oral presentations and written exams. This work discusses the impact of the proposed design project as an effective tools for teaching kinematics and dynamics of machinery in a non-traditional manner.
This article presents the design and implementation of electronic modules for protection, voltage monitoring, and safe reset aimed at industrial applications that demand high reliability in terms of electrical safety. The developed system continuously monitors single-phase 220 VAC and three-phase (220 VAC, 380 VAC, and 440 VAC) voltage equipment and tools, integrating voltage measurement circuits and using an ATTiny 84 microcontroller for phase loss detection and protection processing, incorrect phase sequence in three-phase voltages, or abnormal power supply conditions with historical data in three-phase applications. It also enables safe reset reactivation, in addition to providing protection against incorrect rotation reversal in motors and faster short-circuit faults. The main objective is to prevent uncontrolled start-up when recovering a power supply lost due to external problems and to ensure that, after a power outage, power is restored only through a controlled and validated reset. The implementation of the proposed module reduces electrical incidents associated with unsafe resetting by up to 81.2%, thanks to the verification of phase failure or absence of power supply and manual validation prior to service restoration using safe resetting. In addition, the visual interface of LED indicators facilitates rapid identification of the operating status, reducing diagnostic times for problems external to the equipment. Experimental results and field tests demonstrate that the proposed solution constitutes a significant advance in the protection of industrial machinery, increasing workplace safety, the reliability of the electrical system, and the operational sustainability of power tools.
One of the important directions in the modern development of the scientific school of applied geometry at the National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute" is the methodology of structural and parametric shaping of various technical objects. Significant theoretical and practical achievements have been made in this field in recent years. In particular, this applies to implementations in the aviation industry, general mechanical engineering, and other sectors of the national economy. The current difficult period in Ukraine is related to the military actions on its territory. In such conditions, agriculture has become a significant component of our country's economy. Therefore, further improvement of this sector represents a pressing scientific and applied problem. One of the ways to successfully solve the outlined problems is, in particular, the improvement of the used tillage tools. In this regard, defining the optimal shape and dimensions of these technical objects is considered progressive. These factors not only significantly improve the quality of soil cultivation but also contribute to the preservation of its fertility, reduction of energy consumption in the corresponding technological processes, and so on. The application of computer-based structural and parametric geometric modeling enables productive and flexible implementation of multi-faceted analysis of a large number of design options during the development of industrial products. This approach ensures comprehensive optimization, including various tillage implements. An important aspect of this process is the use of integrated generalized geometric models that encompass a wide range of technical objects. This concept significantly enhances the efficiency of existing computer information technologies. This article reviews some fundamental results obtained regarding the structural and parametric shaping of certain agricultural machinery products. For example, this includes tillage disks, chisel plows, and moldboard plows. The publication also presents proposed new methods and techniques of geometric modeling, outlines their development directions, and identifies the prospects for conducting further related scientific research.
This paper reports performance improvement at KSEW by implementing Lean System. KSEW operation had historically suffered from work delay, cost overruns, material wastage, low manpower utilization, and low worker morale. Implementation of lean tools (5S, SMART QMS Objectives, RCA. 5 Whys, Kaizen, JIT and Poka Yoke with CAPA) at KSEW minimized work order delays, eliminated breakdowns, increased workspace, reduced sorting time, rework & material waste. Productive utilization of manpower & machinery resulted in improved quality, customer satisfaction, and overall performance, production and profit. Promising results at KSEW present a case study & benchmark model on implementing Lean System in the port & shipping industry.
Daniel J. France, Paromita Nath, Jason Slagle
et al.
Abstract Background A common cause of preventable harm is the failure to detect and appropriately respond to clinical deterioration. Timely intervention is needed, particularly in medically complex patients, to mitigate the effects of adverse events, disease progression, and medical error. This challenging problem requires clinical surveillance, early recognition, timely notification of the appropriate clinicians, and effective intervention. Objectives We determined the feasibility of designing, developing, and implementing the tools and processes to create a surveillance-and-risk prediction system to detect clinical deterioration in cancer outpatients. Methods We used systems engineering and iterative human-centered design to develop a functional prototype of a surveillance-and-risk prediction system. The system includes passive surveillance involving wearable sensors, active surveillance involving patient event and symptom reporting as well as extraction of selected patient data from the electronic health record (EHR), a predictive model, and communication of estimated risk to clinicians. System usability was evaluated using patient and clinician interviews and clinician ratings using the System Usability Scale (SUS). Results Fifty of 71 recruited patients enrolled in the feasibility study. Patient-reported outcome measures and clinical data extracted from the EHR were the best predictors of a patient's 7-day risk of experiencing unplanned treatment events (UTEs, i.e., emergency room visits, hospital admissions, or major treatment changes). Deep learning neural network models using these predictors demonstrated modest performance in predicting 7-day UTE risk (PROMS, F-measure: 0.900, area under the receiver operating characteristic curve [AUC-ROC]: 0.983; clinical data from EHR F-measure: 0.625, AUC-ROC: 0.983). Patient risk scores were communicated to clinicians using a risk communication prototype rated favorably by clinicians with a SUS score of 76 out of 100 (median = 80; range: 60–85). Conclusion We demonstrate the feasibility of a surveillance-and-risk prediction system for detecting and reporting clinical deterioration in cancer outpatients. Future research is needed to fully implement and evaluate system adoption and effectiveness under different clinical situations.
S. P. Suryodiningrat, Harjanto Prabowo, Arief Ramadhan
et al.
Every nation is interested in the best practices of those with high enrolment in vocational schools and low young unemployment rates. We'd like to build robust systems that can deal with issues like the quick pace of technology development and the mismatch between supply and demand in the job market. Since Mark Zuckerberg rebranded the company as Meta in 2021, the Metaverse has seen a rise in popularity. The same can be said about mixed reality, which is currently in the spotlight following the release of Microsoft's first HoloLens. The authors propose a mixed reality as a means of resolving the problem of inadequately meeting the demand for skilled workers. The purpose of this study is to help machinery vocational schools decide whether or not to adopt a mixed reality as a teaching and learning tool by identifying the most important components of a metaverse-based mixed reality for such institutions and which metaverse type mixed reality belongs to. This study uses a methodology that is developed by the authors to accommodate the true or natural flow of this research. The results of this study are to provide the essential elements of mixed reality systems as the mixed reality systems are not part of any metaverse type. The implication of this study is since mixed reality is the combination of several metaverse types, the essential elements of mixed reality systems are taken from the components of the other metaverse types. The results of this study are to be a guide of what needs to be prepared before implementing a mixed reality system.
The application of blasting in modern engineering construction is prized for its speed, efficiency, and cost-effectiveness. However, the resultant vibrations can have significant adverse effects on surrounding buildings and residents. The challenge of optimizing blasting procedures to satisfy excavation needs while minimizing vibration impacts is a critical concern in blasting excavation. This research addresses this challenge through the development of a 3D simulation and analysis model for an underground pumped storage power plant in East China, utilizing the LS-DYNA finite element analysis software. To explore the influence of charging structures on rock fragmentation and vibration propagation, three distinct blasting programs were formulated, each featuring varied configurations within the machinery room. The analysis revealed that the adoption of an optimized charging structure can significantly decrease damage to the protective layer by approximately 40%, while also reducing the impact on the upstream and downstream side walls by 27.25% and 12.03%, respectively, without compromising the efficacy of the main blast zone. Moreover, the vibration velocities at the remote measurement point were found to be reduced across multiple directions, indicating effective control of the vibration effects. The post-implementation of the optimized blasting strategy at the site, the assessment of the retained surrounding rock integrity, and the impact on protected structures demonstrated that the proposed solution met satisfactory outcomes. This study underscores the potential of simulation-based optimization in managing vibration risks during blasting operations, offering a valuable tool for engineers and practitioners in the field of underground construction.
In this project, a smart knee sleeve was developed for the purpose of measuring a subject’s knee angle continually. The device is wireless and washable, making it suitable for rehabilitation at home. Two separate methods were incorporated onto a standard knee sleeve: a flexible silicone-based bend sensor and two IMUs. Each approach was evaluated, and testing was conducted on three subjects wearing the knee sleeve, using a reference video motion-tracking method. Squats were used as the exercise protocol for testing. The results showed that the flex sensor performed better for two of the three participants, with an average RMSE of 8.3 degrees, which is comparable to results from related research.
Maria Mercedes Gamboa-Medina, Fabrizio Silva Campos
Short-time forecasting of the demand on water distribution networks is a challenging task because of the high variability and uncertainty of that demand. Of the different approaches used, we consider the probability modeling of demand time series to be the most interesting, and specifically propose the use of Generalized Autoregressive Moving Average (GARMA) models. The complete proposed model uses a gamma probability density function, variables for weekends, and harmonic functions for daily and weekly seasonality, among other parameters. In the context of the Battle of Water Demand Forecasting, we train and test the model with a demand database for ten District Metered Areas. We obtain high accuracy, with mean absolute error values of around 0.25 L/s to 1.89 L/s.
The workforce scheduling problem for cell production lines has been recognized as one of the significant issues in manufacturing systems. In most of previous studies, complex workers’ operations in a cell such as traveling times of multi-skilled operators in another cell to their workplaces, have not been considered in the workforce optimization model. In this paper, we consider the simultaneous optimization of the product input sequence and workforce assignment scheduling problem for multi-stage multi-item cell production lines. The problem is formulated as a resource-constrained project scheduling problem (RCPSP). We propose an RCPSP formulation of the scheduling problem for multi-product cell production lines where the traveling time of operators and products are considered. The exact solution of the RCPSP formulation is derived by using a general-purpose solver, Gurobi. The validity of the RCPSP model is verified by comparing it with a genetic algorithm (GA) using a commercial simulation software (Siemens Plant Simulation). Experimental results show that the derived exact solution of RCPSP is better than that of the solutions derived by the GA algorithm in the Siemens Plant Simulation.
Engineering machinery, tools, and implements, Mechanical engineering and machinery
The present study addresses the pervasive issue of delays in machinery repair services within the mining and construction sectors, which are critical to maintaining operational efficiency. The motivation for this research stems from the significant negative impacts of these delays, including missed deadlines, customer dissatisfaction, and financial losses. The study aims to mitigate these challenges by implementing Lean Service methodologies, including 5S, Kaizen, and Value Stream Mapping (VSM), to optimize repair shop processes. The proposed improvement model consists of six stages: Lean Solution, Lean Education, Lean Application, Monitoring and Control, Feedback, and Continuous Improvement. Each stage focuses on specific aspects of process enhancement, from establishing a quality policy and training staff in Lean principles to implementing practical tools such as 5S for workspace organization and Poka Yoke for error prevention. The model also incorporates continuous monitoring, feedback systems, and the Kaizen philosophy to sustain long-term improvements. The implementation of this model yielded notable results. The repair time was reduced by 46%, showcasing a substantial decrease in turnaround time. The 5S audit score improved by 46.27%, indicating enhanced organization and cleanliness in the work environment. Productivity increased by 26.31%, reflecting a higher capacity for repair activities. Additionally, the number of units in process was reduced by 55.55%, demonstrating improved workflow and efficiency. These indicators confirm the effectiveness of the Lean Service approach in addressing delays and enhancing overall operational efficiency in machinery repair services.