Hasil untuk "Industrial directories"

Yang Dinghua, Zhao Guohan, Shen Zhiyong et al.

Establishment of transmission chain system dynamics equation: the pure torsion elastic dynamic model of wind turbine transmission chain system is established by using the Dalembert principle. In the process of establishing the dynamic equations, the time-varying wind torque obtained by the wind wheel is taken as the external load. The average wind speed is 11 m/s, the standard deviation of wind speed is 2.31, the Weibull distribution scale parameter C is the tie value of the annual average of the three wind towers is 8.179, and the average value of the annual average of the shape parameter of the three wind towers K is 2.938. Dynamic response of transmission chain system: using MATLAB through Runge-Kutta method, programming the system dynamic response, get the wind turbine transmission system in the external excitation and internal excitation of the transmission system, such as disc parts vibration displacement, vibration speed, gear between dynamic engagement force, and the inherent mode of the whole system. Modal analysis of the transmission chain reveals three key natural frequencies: 18.6 Hz (dominant mode from low-speed shaft), 36.4 Hz (from first-stage planetary gears), and 57.8 Hz (from high-speed parallel gear). These frequencies are significantly higher than the turbine's operational frequency range (0.3–5 Hz, corresponding to rotor speed 19 r/min and generator speed 1800 r/min), confirming no torsional resonance risk during normal operation.

David Kube, Simon Hadwiger, Tobias Meisen

Robotic foundation models (RFMs) are emerging as a promising route towards flexible, instruction- and demonstration-driven robot control, however, a critical investigation of their industrial applicability is still lacking. This survey gives an extensive overview over the RFM-landscape and analyses, driven by concrete implications, how industrial domains and use cases shape the requirements of RFMs, with particular focus on collaborative robot platforms, heterogeneous sensing and actuation, edge-computing constraints, and safety-critical operation. We synthesise industrial deployment perspectives into eleven interdependent implications and operationalise them into an assessment framework comprising a catalogue of 149 concrete criteria, spanning both model capabilities and ecosystem requirements. Using this framework, we evaluate 324 manipulation-capable RFMs via 48,276 criterion-level decisions obtained via a conservative LLM-assisted evaluation pipeline, validated against expert judgements. The results indicate that industrial maturity is limited and uneven: even the highest-rated models satisfy only a fraction of criteria and typically exhibit narrow implication-specific peaks rather than integrated coverage. We conclude that progress towards industry-grade RFMs depends less on isolated benchmark successes than on systematic incorporation of safety, real-time feasibility, robust perception, interaction, and cost-effective system integration into auditable deployment stacks.

Li Wang, Wanxin Xu, Jiang Li

This study investigated safety management performance in small- and medium-sized private coal mining enterprises (SMPCMEs) through an integrated application of the 24Model accident causation theory and fuzzy-set qualitative comparative analysis (fsQCA). Analyzing 40 sudden incidents (2013–2023), we examined six key factors—organizational, individual, and external dimensions—to identify nonlinear risk pathways. Results revealed four critical failure types—Internally Balanced (cultural–behavioral–environmental collapse), Safety Culture–Deficient (institutional hollowing), Cultural–External Environment (policy-implementation paradox), and External Environment–Integrated (technological-regulatory failure)—that collectively explained 83% of performance variance. Tailored strategies, including IoT-based real-time monitoring and AI-driven inspections, are proposed to transition from fragmented interventions to systemic governance. These findings provide actionable insights for enhancing safety resilience in high-risk mining sectors.

Hyun Jeong Seo, Seung-Yoon Rhee, Nam Kyun Kim

(1) Background: The dynamic nature of workplaces highlights the urgent need for effective strategies to promote a safe working environment and enhance workers’ well-being. These strategies must address both organizational safety performance and individual safety behaviors. (2) Methods: This study employed the job demands-resources (JD-R) model to examine the impact of workplace factors on safety behaviors and organizational safety performance among 3255 manufacturing companies. The data utilized in this study originate from the 10th Occupational Safety and Health Status Survey by the Korea Occupational Safety and Health Research Institute (KOSHA). (3) Results: Occupational stressors involving physical and psychological risks (job demand) significantly reduced employees’ adherence to safety practices while increasing workplace diseases and accidents. Conversely, when job resources were effectively mobilized to bolster individual resources, safety behaviors improved, and the incidence of workplace diseases and accidents decreased. Middle managers’ safety competency emerged as a critical moderating factor in these relationships, amplifying the positive impact of job resources. (4) Conclusions: The results highlight the necessity of managing physical and emotional hazards while enhancing middle managers’ abilities to promote workplace safety. A multidimensional approach is essential for preventing workplace accidents and improving safety outcomes. Implementing a comprehensive stress management system is particularly vital to safeguarding workers in the manufacturing industry.

Shadi Amer, Dina Yamin, Nurul Ainun Hamzah et al.

Introduction: In 21st century, computers are crucial devices in universities’ official operations. In academic institutions, musculoskeletal disorders (MSDs) are leading causes of decreased productivity, absenteeism, disability, and illness. Office staff who use computers extensively are vulnerable to occupational MSDs. This study aims to determine risk factors of MSDs among computer users in a public university. Methods: This cross-sectional study involved 320 respondents among computer users working in all departments in Universiti Sains Malaysia Health Campus using random sampling. Tools used were a self-administered questionnaire containing questions on socio-demographical data, Cornell Musculoskeletal Discomfort Questionnaire (CMDQ) for assessing musculoskeletal disorder and observation and Rapid Office Strain Assessment (ROSA) to assess office equipment and quantify exposure to risk factors in office work environment. Results: Response rate was 92% and 86.2% of respondents reported work-related musculoskeletal disorders (WRMSDs). The most prevalent MSD was lower back, 62.8% of MSD cases, followed by right shoulder (53.4%), hip/buttock (46.4%), and left shoulder (45.3%).Older age was significantly associated with WRMSDs (OR=6.944, CI:1.238-39.017, p=0.0.028) and with neck MSDs (OR=3.908, CI:1.342-11.377, p=0.012), while female gender was significantly associated with neck MSDs (OR=2.042, CI:1.199-3.475, p=0.009) and with upper arm MSDs (OR=1.791, CI:1.091-2.941, p=0.021). Older age was significantly associated with upper arm MSDs (OR=3.303, CI:1.006-10.849, p=0.049), while those with healthy and overweight were significantly associated with upper arm MSDs (OR=0.092, CI:0.010-0.814, p=0.046), (OR=0.127, CI:0.014-1.123, p=0.032), respectively. Conclusion: The prevalence of reported WRMSDs and MSDs at neck and upper arm were associated with socio-demographic background and high duration of computer use; 12.2% of workstation presented musculoskeletal discomfort risk.

Huang Tangsen, Yin Xiangdong, Jiang Ensong

Brain tumors, abnormal growths in the brain or spinal canal, can be benign or malignant, causing symptoms like headaches, seizures, and cognitive decline by disrupting brain function. Therefore, developing reliable predictive models for diagnosis and prognosis is crucial. In this paper, the prediction of brain tumors is made using machine learning models enhanced by an optimizer, namely Escaping Bird Search Optimization. Optimized models incorporate Ada Boost Classifier (ADEB), Gaussian Process Classifier (GPEB), and Support Vector Classifier (SVC) which, after being tested on a few databases, were named ADEB, SVEB, and GPEB, respectively, and their predictive power was assessed. The best single model performance overall on all databases is the SVC with an average accuracy of 0.981, while among enhanced models, the optimized model, called SVEB, using SVC, attained the highest accuracy for all models and reached as high as 0.990. These findings underscore the role of optimization techniques and demonstrate the effectiveness of machine learning in predicting brain cancers. The improved performance of the enhanced SVC model, SVEB, suggests it could offer a reliable approach for accurate brain tumor prediction. Enhanced patient outcomes and early diagnosis could be an implication of this in the field of neuro-oncology.

Spyridon Damilos, Dionisis Semitekolos, Stratos Saliakas et al.

Carbon fibre-reinforced polymers (CFRPs) are a lightweight alternative solution for various applications due to their mechanical and structural properties. However, debonding at the fibre–matrix interface is an important failure mechanism in composite materials. Proposed solutions include using nano-scale reinforcements to strengthen and toughen structural composites. This study covers a comprehensive approach for evaluating occupational hazards during the sizing of 6k carbon fibres using multi-walled functionalized carbon nanotubes (MWCNTs) and few-layer graphene (FLG) at a pilot scale. Material hazard and exposure banding showed elevated risks of exposure to nanomaterials during the sizing process, while a ‘what-if’ process hazard analysis allowed for the evaluation of hazard control options against the hypothetical process failure scenarios of human error and utilities malfunctioning. On-site measurements of airborne particles highlighted that using MWCNTs or FLG as a sizing agent had negligible effects on the overall exposure potential, and higher micro-size particle concentrations were observed at the beginning of the process, while particle size distribution showcased high concentrations of particles below 50 nm. This analysis provides a thorough investigation of the risks and potential exposure to airborne hazardous substances during CF sizing while providing insights for the effective implementation of a safe-by-design strategy for designing targeted hazard control systems.

Nur Septia Handayani, Berliana Devianti Putri, Iwan Muhamad Ramdan

Introduction: In Indonesia, over 1000 healthcare workers have died due to COVID-19. Healthcare workers face increased workloads and negative perceptions, including discrimination and verbal or physical violence, which may impact their quality of life. Health-related quality of life encompasses both physical (PCS) and mental (MCS) health components. This study aims to analyze the health-related quality of life of healthcare workers who are obliged to service during the COVID-19 pandemic in Indonesia and occupational health and safety factors based on the workplace location risk zone. Methods: A cross-sectional online survey was conducted involving 149 healthcare workers from several areas of Indonesia as representatives from the red and orange risk zones. Health-related quality of life was measured using the SF-36 questionnaire. Differences in health-related quality of life scores were analyzed using Mann-Whitney test base on COVID-19 risk Zone and PPE availability. Results: Healthcare workers in the lower-risk (orange zone) exhibited better mental health scores (MCS 75±15.5) compared to those in the high-risk zone (red zone) (MCS 66.2±15.2). Additionally, those who received a complete set of PPE from their workplace had better health-related quality of life scores workplace (MCS 76.9±14.2, PCS 77±16) than those who lacked such provision (MCS 73±17.6, PCS 82±13.4). Furthermore, healthcare workers with access to PCR testing at their workplace tended to have higher quality of life scores than those who only had access to rapid testing. Conclusion: These findings highlight how the Health System addresses the pandemic, particularly regarding the health and safety of healthcare workers

Johnell O. Brooks, Rakesh Gangadharaiah, Patrick J. Rosopa et al.

Clinicians who do not specialize in driving have a need for simple assessment tools for both the aging population and new drivers. While many researchers focus on complex driving scenarios presented on simulators or on-road driving, this exploratory study examines the use of interactive exercises presented using a driving simulator to determine if there are differences in the speed at which senior and novice teen drivers respond to the steering wheel and pedal stimuli. This gap is addressed by evaluating performance differences between 34 senior drivers (over 60) and 17 novice teen drivers (ages 16–17) using interactive exercises with a driving simulator: Reaction Timer Steering©, Reaction Timer Stoplight©, and Stoplight and Steering©. Overall, teens had faster reaction times and fewer errors than seniors, yet seniors demonstrated greater improvement over time. Reaction times decreased for both age groups using the Reaction Timer Stoplight exercise. For the Stoplight and Steering exercise, significant differences between the groups were identified for both the number of errors as well as their reaction times. The findings from this exploratory study suggest the potential value of using driving simulators for assessment and potentially training the motor movements associated with driving across different age groups. By providing safe and controlled environments, simulators offer value to clinicians and educators for evaluations, interventions, and skill screenings to potentially improve safety for at-risk driver populations.

Yu Sha, Ningtao Liu, Haofeng Liu et al.

Cavitation intensity recognition (CIR) is a critical technology for detecting and evaluating cavitation phenomena in hydraulic machinery, with significant implications for operational safety, performance optimization, and maintenance cost reduction in complex industrial systems. Despite substantial research progress, a comprehensive review that systematically traces the development trajectory and provides explicit guidance for future research is still lacking. To bridge this gap, this paper presents a thorough review and analysis of hundreds of publications on intelligent CIR across various types of mechanical equipment from 2002 to 2025, summarizing its technological evolution and offering insights for future development. The early stages are dominated by traditional machine learning approaches that relied on manually engineered features under the guidance of domain expert knowledge. The advent of deep learning has driven the development of end-to-end models capable of automatically extracting features from multi-source signals, thereby significantly improving recognition performance and robustness. Recently, physical informed diagnostic models have been proposed to embed domain knowledge into deep learning models, which can enhance interpretability and cross-condition generalization. In the future, transfer learning, multi-modal fusion, lightweight network architectures, and the deployment of industrial agents are expected to propel CIR technology into a new stage, addressing challenges in multi-source data acquisition, standardized evaluation, and industrial implementation. The paper aims to systematically outline the evolution of CIR technology and highlight the emerging trend of integrating deep learning with physical knowledge. This provides a significant reference for researchers and practitioners in the field of intelligent cavitation diagnosis in complex industrial systems.

Artemis Stefanidou, Panagiotis Radoglou-Grammatikis, Vasileios Argyriou et al.

The recent tremendous advancements in the areas of Artificial Intelligence (AI) and Deep Learning (DL) have also resulted into corresponding remarkable progress in the field of Computer Vision (CV), showcasing robust technological solutions in a wide range of application sectors of high industrial interest (e.g., healthcare, autonomous driving, automation, etc.). Despite the outstanding performance of CV systems in specific domains, their development and exploitation at industrial-scale necessitates, among other, the addressing of requirements related to the reliability, transparency, trustworthiness, security, safety, and robustness of the developed AI models. The latter raises the imperative need for the development of efficient, comprehensive and widely-adopted industrial standards. In this context, this study investigates the current state of play regarding the development of industrial computer vision AI standards, emphasizing on critical aspects, like model interpretability, data quality, and regulatory compliance. In particular, a systematic analysis of launched and currently developing CV standards, proposed by the main international standardization bodies (e.g. ISO/IEC, IEEE, DIN, etc.) is performed. The latter is complemented by a comprehensive discussion on the current challenges and future directions observed in this regularization endeavor.

Jiawen Yu, Jieji Ren, Yang Chang et al.

Anomaly detection and localization in automated industrial manufacturing can significantly enhance production efficiency and product quality. Existing methods are capable of detecting surface defects in pre-defined or controlled imaging environments. However, accurately detecting workpiece defects in complex and unstructured industrial environments with varying views, poses and illumination remains challenging. We propose a novel anomaly detection and localization method specifically designed to handle inputs with perturbative patterns. Our approach introduces a new framework based on a collaborative distillation heterogeneous teacher network (HetNet), an adaptive local-global feature fusion module, and a local multivariate Gaussian noise generation module. HetNet can learn to model the complex feature distribution of normal patterns using limited information about local disruptive changes. We conducted extensive experiments on mainstream benchmarks. HetNet demonstrates superior performance with approximately 10% improvement across all evaluation metrics on MSC-AD under industrial conditions, while achieving state-of-the-art results on other datasets, validating its resilience to environmental fluctuations and its capability to enhance the reliability of industrial anomaly detection systems across diverse scenarios. Tests in real-world environments further confirm that HetNet can be effectively integrated into production lines to achieve robust and real-time anomaly detection. Codes, images and videos are published on the project website at: https://zihuatanejoyu.github.io/HetNet/

Jiaqi Hu, Hongli Xu, Junwen Huang et al.

Accurate 6D pose estimation is essential for robotic manipulation in industrial environments. Existing pipelines typically rely on off-the-shelf object detectors followed by cropping and pose refinement, but their performance degrades under challenging conditions such as clutter, poor lighting, and complex backgrounds, making detection the critical bottleneck. In this work, we introduce a standardized and plug-in pipeline for 2D detection of unseen objects in industrial settings. Based on current SOTA baselines, our approach reduces domain shift and background artifacts through low-light image enhancement and background removal guided by open-vocabulary detection with foundation models. This design suppresses the false positives prevalent in raw SAM outputs, yielding more reliable detections for downstream pose estimation. Extensive experiments on real-world industrial bin-picking benchmarks from BOP demonstrate that our method significantly boosts detection accuracy while incurring negligible inference overhead, showing the effectiveness and practicality of the proposed method.

Muhammad Iman Tsalatsa Raihan Tjahjono, Adhitya Ryan Ramadhani

Introduction: An oil and gas refinery operates various equipment with specific functions for different processes. Each piece of equipment has potential hazards that can damage the equipment and injure or kill workers. This study focuses on the hydrotreated vegetable oil (HVO) export facility from the jetty loading area at an oil and gas company that processes flammable liquid using various equipment. Methods: The HAZOP method determined the hazardous spots, and the probability of each equipment failure corresponding to the system was also determined using fault tree analysis (FTA). Furthermore, every event tree analysis (ETA) output probability was also determined. The probability and radius of pool fire varied for different leak hole scenarios. The final steps are individual risk per annum and potential loss of life to measure the risk level of the system. Results: Based on HAZOP deviation scenarios, every operating equipment can potentially cause a pool fire. In FTA, scenarios were developed based on different leakage hole sizes, ranging from 1-3 mm, 3-10 mm, 10-50 mm, 150 mm, and >150 mm. The results indicated that leakage could occur across all operating equipment. Similarly, the ETA applied the same bore size scenarios. The consequence analysis yielded a worst-case outcome of pool fire and a best-case outcome of un-ignited fluid release. Subsequently, the pool fire output was modeled using ALOHA, which resulted in three heat flux zones: the red zone (10 kW/m²), the orange zone (5 kW/m²), and the yellow zone (2 kW/m²). Smaller leak holes had a higher probability but smaller pool fire radius. The initial risk of the export facility was unacceptable. Furthermore, insufficient safeguards contribute significantly to the resulting high-risk level. Two mitigations were implemented: adding safeguards and reducing worker hours. Conclusion: The final results showed that for every piece of equipment, the overall risk of the export facility became acceptable after mitigation..

Tommaso Martone, Pietro Iob, Mauro Schiavo et al.

This paper explores the potential benefits and challenges of integrating Electric Vehicles (EVs) and Autonomous Ground Vehicles (AGVs) in industrial settings to improve sustainability and operational efficiency. While EVs offer environmental advantages, barriers like high costs and limited range hinder their widespread use. Similarly, AGVs, despite their autonomous capabilities, face challenges in technology integration and reliability. To address these issues, the paper develops a fleet management tool tailored for coordinating electric AGVs in industrial environments. The study focuses on simulating electric AGV performance in a primary aluminum plant to provide insights into their effectiveness and offer recommendations for optimizing fleet performance.

Daniel Gramelt, Timon Höfer, Ute Schmid

Being able to recognise defects in industrial objects is a key element of quality assurance in production lines. Our research focuses on visual anomaly detection in RGB images. Although Convolutional Neural Networks (CNNs) achieve high accuracies in this task, end users in industrial environments receive the model's decisions without additional explanations. Therefore, it is of interest to enrich the model's outputs with further explanations to increase confidence in the model and speed up anomaly detection. In our work, we focus on (1) CNN-based classification models and (2) the further development of a model-agnostic explanation algorithm for black-box classifiers. Additionally, (3) we demonstrate how we can establish an interactive interface that allows users to further correct the model's output. We present our NearCAIPI Interaction Framework, which improves AI through user interaction, and show how this approach increases the system's trustworthiness. We also illustrate how NearCAIPI can integrate human feedback into an interactive process chain.

Giampaolo Cuozzo, Enrico Testi, Salvatore Riolo et al.

The Industrial Internet of Things (IIoT) paradigm has emerged as a transformative force, revolutionizing industrial processes by integrating advanced wireless technologies into traditional procedures to enhance their efficiency. The importance of this paradigm shift has produced a massive, yet heterogeneous, proliferation of scientific contributions. However, these works lack a standardized and cohesive characterization of the IIoT framework coming from different entities, like the 3rd Generation Partnership Project (3GPP) or the 5G Alliance for Connected Industries and Automation (5G-ACIA), resulting in divergent perspectives and potentially hindering interoperability. To bridge this gap, this article offers a unified characterization of (i) the main IIoT application domains, (ii) their respective requirements, (iii) the principal technological gaps existing in the current literature, and, most importantly, (iv) we propose a systematic approach for assessing and addressing the identified research challenges. Therefore, this article serves as a roadmap for future research endeavors, promoting a unified vision of the IIoT paradigm and fostering collaborative efforts to advance the field.

Chanjun Park, Minsoo Khang, Dahyun Kim

This paper delves into the contrasting roles of data within academic and industrial spheres, highlighting the divergence between Data-Centric AI and Model-Agnostic AI approaches. We argue that while Data-Centric AI focuses on the primacy of high-quality data for model performance, Model-Agnostic AI prioritizes algorithmic flexibility, often at the expense of data quality considerations. This distinction reveals that academic standards for data quality frequently do not meet the rigorous demands of industrial applications, leading to potential pitfalls in deploying academic models in real-world settings. Through a comprehensive analysis, we address these disparities, presenting both the challenges they pose and strategies for bridging the gap. Furthermore, we propose a novel paradigm: Model-Based Data-Centric AI, which aims to reconcile these differences by integrating model considerations into data optimization processes. This approach underscores the necessity for evolving data requirements that are sensitive to the nuances of both academic research and industrial deployment. By exploring these discrepancies, we aim to foster a more nuanced understanding of data's role in AI development and encourage a convergence of academic and industrial standards to enhance AI's real-world applicability.

Srinjoy Mitra, Jean-Pierre Raskin

This paper discusses the importance of reflective and socially conscious education in engineering schools, particularly within the EE/CS sector. While most engineering disciplines have historically aligned themselves with the demands of the technology industry, the lack of critical examination of industry practices and their impact on justice, equality, and sustainability is self-evident. Today, the for-profit engineering/technology companies, some of which are among the largest in the world, also shape the narrative of engineering education and research in universities. As engineering graduates form the largest cohorts within STEM disciplines in Western countries, they become future professionals who will work, lead, or even establish companies in this industry. Unfortunately, the curriculum within engineering education often lacks a deep understanding of social realities, an essential component of a comprehensive university education. Here we establish this unusual connection with the industry that has driven engineering higher education for several decades and its obvious negative impacts to society. We analyse this nexus and highlight the need for engineering schools to hold a more critical viewpoint. Given the wealth and power of modern technology companies, particularly in the ICT domain, questioning their techno-solutionism narrative is essential within the institutes of higher education.

Hendrik Schricker, Benedikt Schuler, Christiane Reinert et al.

Industrial decision-makers often base decisions on mathematical optimization models to achieve cost-efficient design solutions in energy transitions. However, since a model can only approximate reality, the optimal solution is not necessarily the best real-world energy system. Exploring near-optimal design spaces, e.g., by the Modeling All Alternatives (MAA) method, provides a more holistic view of decision alternatives beyond the cost-optimal solution. However, the MAA method misses out on discrete in-vestment decisions. Incorporating such discrete investment decisions is crucial when modeling industrial energy systems. Our work extends the MAA method by integrating discrete design decisions. We optimize the design and operation of an industrial energy system transformation using a mixed-integer linear program. First, we explore the continuous, near-optimal design space by applying the MAA method. Thereafter, we sample all discrete design alternatives from the continuous, near-optimal design space. In a case study, we apply our method to identify all near-optimal design alternatives of an industrial energy system. We find 128 near-optimal design alternatives where costs are allowed to increase to a maximum of one percent offering decision-makers more flexibility in their investment decisions. Our work enables the analysis of discrete design alternatives for industrial energy transitions and supports the decision-making process for investments in energy infrastructure.