Hasil untuk "Industrial safety. Industrial accident prevention"

Junfan Zhuo, Feng Zhu

Leonard Kleinberger, Michael Gundall, Hans D. Schotten

Industrial 5G deployments using Time Division Duplex (TDD) networks face a critical challenge: existing schedulers rely on static configuration of Uplink (UL) to Downlink (DL) resource ratios, failing to adapt to dynamic asymmetric traffic demands. This limitation is particularly problematic in Industry 4.0 scenarios where traffic patterns exhibit significant asymmetry between directions and heterogeneous Quality of Service (QoS) requirements. We present FLEX, a novel QoS-aware scheduler that dynamically adjusts the UL/DL ratio in flexible TDD slots while respecting diverse QoS requirements. FLEX introduces DL buffer state estimation to prevent starvation of high-priority DL traffic, exploiting the deterministic nature of industrial traffic patterns for accurate predictions. Through extensive simulations of industrial scenarios using 5G LENA and ns-3, we demonstrate that FLEX achieves similar throughput compared to established scheduling while correctly enforcing QoS priorities in both traffic directions. For deterministic traffic patterns, FLEX maintains minimal latency overhead (less than 1 slot duration), making it particularly suitable for industrial automation applications.

Flavien Armangeon, Thibaud Ehret, Enric Meinhardt-Llopis et al.

Aligning functional schematics with 2D and 3D scene acquisitions is crucial for building digital twins, especially for old industrial facilities that lack native digital models. Current manual alignment using images and LiDAR data does not scale due to tediousness and complexity of industrial sites. Inconsistencies between schematics and reality, and the scarcity of public industrial datasets, make the problem both challenging and underexplored. This paper introduces IRIS-v2, a comprehensive dataset to support further research. It includes images, point clouds, 2D annotated boxes and segmentation masks, a CAD model, 3D pipe routing information, and the P&ID (Piping and Instrumentation Diagram). The alignment is experimented on a practical case study, aiming at reducing the time required for this task by combining segmentation and graph matching.

Farzana Yeasmin, Aaron J. E. Bach, Jean P. Palutikof et al.

Objective: The ready-made garment (RMG) sector is pivotal to Bangladesh’s economy, providing export opportunities and employment. To ensure sustained productivity and a thriving workforce, workplace hazards like heat must be acknowledged, assessed and managed. This paper explores heat impacts on health and productivity of production-line workers in two RMG factories of Bangladesh. Methods: Focus group discussions and in-depth interviews were conducted with the workers of two RMG factories in Dhaka in 2022 to identify perceived heat-related health and productivity impacts and explore barriers to workers accessing heat-related medical care. Key informant interviews were conducted with factory officials, onsite health professionals, government officials, the RMG peak body, and non-government organisation professionals with expertise in industry and workplace issues. Results: Workers and health professionals attributed symptoms like headaches, dizziness, fatigue and nausea to heat. Factory health professionals observed changes in cardiovascular strain (eg, altered blood pressure responses) in workers during summer. Other key informants identified higher absenteeism across summer. Heat was identified as an impediment to overall productivity by workers themselves and others working across the sector. Conclusion: This qualitative study identified how heat exposure in indoor work environments of RMG in Bangladesh influences health of workers and how productivity is influenced directly by heat but also indirectly via necessary cooling measures to reduce heat strain that take workers away from the production line. Despite knowledge of access to hydration as an important heat health risk mitigation strategy, quota pressures inherent in these factories restrict the use of this vital measure.

Agnieszka Gajek

For establishments subject to the requirements of the Seveso III Directive, i.e. establishments with a high or increased risk of a major industrial accident (so-called Seveso establishments), the legal requirements set out in the Directive apply. A separate group of establishments are those which are not lower- or upper-tier establishments, but which nevertheless pose a risk of events equivalent to a major industrial accident. These establishments can be called non-Seveso establishments. On the basis of the requirements for upper-tier and lower-tier establishments in the Seveso III Directive, legal requirements have been prepared for non-Seveso establishments covering eligibility criteria, an Major Accident Prevention Policy and a Safety Management System. The proposed legislation also addresses the issue of supervision and inspection by the competent authorities. The first procedure relates to the qualification of an establishment, so qualification criteria have been proposed to determine whether an establishment falls into the non-Seveso category. With regard to the Major Accident Prevention Policy, the greatest emphasis was placed on establishment management awareness of the hazards posed by the establishment and the potential impact on local residents and informing them of the risks and how to act in the event of a major accident. As the Major Accident Prevention Policy must be implemented through a Safety Management System, the requirements for such a system have been developed.

Sri Handayani, Syarifah Nuraini, Yunita Fiitrianti et al.

Introduction: Since the first case of COVID-19 was detected in Indonesia, the government has implemented Large-Scale Social Restrictions to control the spread of the disease. However, these restrictions have had a negative impact on the economy. To address this, the government has introduced a new normal policy to restore activities while managing the risk of transmission. The government has adopted WHO guidelines through Minister of Health Decree No. 238 of 2020 to ensure COVID-19 workplace safety. This article aims to explore the barriers to COVID-19 workplace safety among Indonesian office workers. Methods: This qualitative research was conducted in DKI Jakarta and Surabaya from September to October 2020. In-depth interviews and observations were conducted with 22 informants selected purposefully. Thematic analysis was used, drawing on the Social-ecological Model (SEM) theory. Results: At the intrapersonal level, fear and perception barriers impact preventive actions against COVID-19. At the interpersonal level, peer influence and perceptions of the work environment affect adherence to office policies. At the organizational level, employee behavior is influenced by socializing, rules, and workplace amenities. Lastly, public policy enforcement is vital at the macro level to reduce risky behaviors among office workers. Conclusion: Implementing comprehensive protocols across different levels is crucial to creating COVID-19 workplace safety. This requires increased public awareness and consistent enforcement, including strengthening organizational policies.

Motoko Ohira, Yoko Ichikawa, Madoka Tsuji et al.

Objectives: Teleworking is a flexible means of working to effectively utilize one’s time and workplace using information and communication technology. However, teleworking can also lead to work–life conflict and health problems. To support the health of teleworkers, this study aimed to elucidate the factors correlated with the self-rated health of teleworkers raising children, focusing on differences between genders. Methods: The study sample included 1,000 teleworkers (500 women and 500 men). Results: The responses to questionnaire items about health differed between men and women. For men, “marital status,” “walks and exercises,” “keeps an uplifted state of mind as much as possible,” and “work-to-family negative spillover” were extracted. For women, “leads a disciplined life,” “keeps an uplifted state of mind as much as possible,” “eating speed compared with others: slower,” and “sufficiently rests through sleep” were found to affect self-rated health. Conclusions: For male teleworkers raising children, sufficient exercise and physical activity is a crucial aspect of health management. For female teleworkers raising children, self-discipline is needed.

Ruimin Ma, Sebastian Zudaire, Zhen Li et al.

Object 6DoF (6D) pose estimation is essential for robotic perception, especially in industrial settings. It enables robots to interact with the environment and manipulate objects. However, existing benchmarks on object 6D pose estimation primarily use everyday objects with rich textures and low-reflectivity, limiting model generalization to industrial scenarios where objects are often metallic, texture-less, and highly reflective. To address this gap, we propose a novel dataset and benchmark namely \textit{Industrial Metallic Dataset (IMD)}, tailored for industrial applications. Our dataset comprises 45 true-to-scale industrial components, captured with an RGB-D camera under natural indoor lighting and varied object arrangements to replicate real-world conditions. The benchmark supports three tasks, including video object segmentation, 6D pose tracking, and one-shot 6D pose estimation. We evaluate existing state-of-the-art models, including XMem and SAM2 for segmentation, and BundleTrack and BundleSDF for pose estimation, to assess model performance in industrial contexts. Evaluation results show that our industrial dataset is more challenging than existing household object datasets. This benchmark provides the baseline for developing and comparing segmentation and pose estimation algorithms that better generalize to industrial robotics scenarios.

Bo Wu

In order to explore whether environmental liability insurance has an important impact on industrial emission reduction, this paper selects provincial (city) level panel data from 2010 to 2020 and constructs a two-way fixed effect model to analyze the impact of environmental liability insurance on carbon emissions from both direct and indirect levels. The empirical analysis results show that: at the direct level, the development of environmental liability insurance has the effect of reducing industrial carbon emissions, and its effect is heterogeneous. At the indirect level, the role of environmental liability insurance is weaker in areas with developed financial industry and underdeveloped financial industry. Further heterogeneity analysis shows that in the industrial developed areas, the effect of environmental liability insurance on carbon emissions is more obvious. Based on this, countermeasures and suggestions are put forward from the aspects of expanding the coverage of environmental liability insurance, innovating the development of environmental liability insurance and improving the level of industrialization.

Taryn DiLorenzo, Xinbao Yu

Haipeng Shao, Chengcheng Xu, Shimul Haque et al.

Wei Zhang, Xianfu Cheng, Yi Zhang et al.

Log parsing, a vital task for interpreting the vast and complex data produced within software architectures faces significant challenges in the transition from academic benchmarks to the industrial domain. Existing log parsers, while highly effective on standardized public datasets, struggle to maintain performance and efficiency when confronted with the sheer scale and diversity of real-world industrial logs. These challenges are two-fold: 1) massive log templates: The performance and efficiency of most existing parsers will be significantly reduced when logs of growing quantities and different lengths; 2) Complex and changeable semantics: Traditional template-matching algorithms cannot accurately match the log templates of complicated industrial logs because they cannot utilize cross-language logs with similar semantics. To address these issues, we propose ECLIPSE, Enhanced Cross-Lingual Industrial log Parsing with Semantic Entropy-LCS, since cross-language logs can robustly parse industrial logs. On the one hand, it integrates two efficient data-driven template-matching algorithms and Faiss indexing. On the other hand, driven by the powerful semantic understanding ability of the Large Language Model (LLM), the semantics of log keywords were accurately extracted, and the retrieval space was effectively reduced. Notably, we launch a Chinese and English cross-platform industrial log parsing benchmark ECLIPSE- BENCH to evaluate the performance of mainstream parsers in industrial scenarios. Our experimental results across public benchmarks and ECLIPSE- BENCH underscore the superior performance and robustness of our proposed ECLIPSE. Notably, ECLIPSE both delivers state-of-the-art performance when compared to strong baselines and preserves a significant edge in processing efficiency.

Jiyu Chen, Jinchuan Qian, Xinmin Zhang et al.

With the development of intelligent manufacturing and the increasing complexity of industrial production, root cause diagnosis has gradually become an important research direction in the field of industrial fault diagnosis. However, existing research methods struggle to effectively combine domain knowledge and industrial data, failing to provide accurate, online, and reliable root cause diagnosis results for industrial processes. To address these issues, a novel fault root cause diagnosis framework based on knowledge graph and industrial data, called Root-KGD, is proposed. Root-KGD uses the knowledge graph to represent domain knowledge and employs data-driven modeling to extract fault features from industrial data. It then combines the knowledge graph and data features to perform knowledge graph reasoning for root cause identification. The performance of the proposed method is validated using two industrial process cases, Tennessee Eastman Process (TEP) and Multiphase Flow Facility (MFF). Compared to existing methods, Root-KGD not only gives more accurate root cause variable diagnosis results but also provides interpretable fault-related information by locating faults to corresponding physical entities in knowledge graph (such as devices and streams). In addition, combined with its lightweight nature, Root-KGD is more effective in online industrial applications.

William Farlessyost, Shweta Singh

Industrial ecosystems are coupled with natural systems through utilization of feedstocks and waste disposal. To ensure resilience in production of industrial systems under the threat of climate change scenarios, it is necessary to evaluate the impact of this coupling on productivity and waste generation. In this work, we present a novel methodology for modeling and assessing the resilience of coupled natural-industrial ecosystems under climate change scenarios. We develop a computationally efficient framework that integrates liquid time-constant (LTC) neural networks as surrogate models to capture complex, nonlinear dynamics of coupled agricultural and industrial systems. The approach is demonstrated through a case study of a soybean-based biodiesel production network in Champaign County, Illinois. LTC models are trained to capture dynamics of nodes and are then coupled and driven by statistically downscaled climate projections for RCP 4.5 and 8.5 scenarios from 2006-2096. The framework enables rapid simulation of system-wide material flow dynamics and exploration of cascading effects from climate-induced disruptions. Results reveal non-linear behaviors and potential tipping points in system resilience under different climate scenarios and farm sizes. The RCP 8.5 scenario led to earlier and more frequent production failures, increased reliance on imports for smaller farms, and complex patterns of waste accumulation and stock levels. The methodology provides valuable insights into system vulnerabilities and adaptive capacities, offering decision support for enhancing the resilience and sustainability of coupled natural-industrial ecosystems in the face of climate change. The framework's adaptability suggests potential applications across various industrial ecosystems and climate-sensitive sectors

Ewen Denney, Ganesh Pai

We propose a new framework to facilitate dynamic assurance within a safety case approach by associating safety performance measurement with the core assurance artifacts of a safety case. The focus is mainly on the safety architecture, whose underlying risk assessment model gives the concrete link from safety measurement to operational risk. Using an aviation domain example of autonomous taxiing, we describe our approach to derive safety indicators and revise the risk assessment based on safety measurement. We then outline a notion of consistency between a collection of safety indicators and the safety case, as a formal basis for implementing the proposed framework in our tool, AdvoCATE.

Markus Voggenreiter, Florian Angermeir, Fabiola Moyón et al.

In recent years, DevOps, the unification of development and operation workflows, has become a trend for the industrial software development lifecycle. Security activities turned into an essential field of application for DevOps principles as they are a fundamental part of secure software development in the industry. A common practice arising from this trend is the automation of security tests that analyze a software product from several perspectives. To effectively improve the security of the analyzed product, the identified security findings must be managed and looped back to the project team for stakeholders to take action. This management must cope with several challenges ranging from low data quality to a consistent prioritization of findings while following DevOps aims. To manage security findings with the same efficiency as other activities in DevOps projects, a methodology for the management of industrial security findings minding DevOps principles is essential. In this paper, we propose a methodology for the management of security findings in industrial DevOps projects, summarizing our research in this domain and presenting the resulting artifact. As an instance of the methodology, we developed the Security Flama, a semantic knowledge base for the automated management of security findings. To analyze the impact of our methodology on industrial practice, we performed a case study on two DevOps projects of a multinational industrial enterprise. The results emphasize the importance of using such an automated methodology in industrial DevOps projects, confirm our approach's usefulness and positive impact on the studied projects, and identify the communication strategy as a crucial factor for usability in practice.

Bani Bandana Ganguly, Shouvik Ganguly, Nitin N. Kadam

Kazunori Ikegami, Hajime Ando, Yasuro Yoshimoto et al.

Objectives: This study examined the relationship of potential advisors — human resources or services that advise workers when they experience health issues that affect their work and work-related health — with psychological distress and analyzed which human resources have a greater impact on improving workers’ mental health. Methods: An Internet-based survey using a self-administered questionnaire was conducted. The target population was workers between the ages of 20 and 69 years. Among a total of 5,111 participants, 4,540 were included in the present analysis. Participants were asked questions regarding potential advisors on work-related health issues. The Kessler 6-item Psychological Distress Scale (K6) was used to assess psychological distress. We used a generalized linear model with a binomial response for assessing the relationship between K6 scores and each potential advisor on work-related health issues. Results: Participants without potential advisors on work-related health issues were significantly more likely to score both K6 ≥5 (cutoff for mild psychological distress) and K6 ≥13 (cutoff for severe psychological distress) than the participants with potential advisors (all p<0.001). The participants for whom a supervisor was the potential advisor on work-related health issues were significantly less likely to score K6 ≥13 than their counterparts (p=0.005). Those for whom an occupational physician or family members was the potential advisor on work-related health issues were significantly less likely to score K6 ≥5 than their counterparts (p=0.011 and p=0.001, respectively). Conclusions: Having potential advisors could be important for workers’ mental health improvement. Specifically, having supervisors, occupational physicians, or family members as potential advisors may be effective in reducing workers’ psychological distress.

Damien Burlet-Vienney, Bertrand Galy, Kariane Cusson Bertrand et al.

Vehicles falling off two-post above-ground (2PAG) lifts is a fairly frequent occurrence. As only limited knowledge is available about the determinants influencing the stability of lifting vehicles with a 2PAG lift, two experimental designs were carried out in order to have quantitative data. This paper addresses support pad slippage as a result of external forces being exerted on a vehicle. The experimental design is based on the consultation of the key players that identify the main issues related to the support pads. The controlled factors chosen in this experimental design were lift support pad type and position, smear on pads, arm locking and external force type. Based on the analysis of variance, factors that had a significant influence on the support pad slippage were (i) support pad type, (ii) external force type and (iii) the interaction between those two controlled factors. Arm locking and support pad position were not statistically significant. From a practical standpoint, initial placement of the support pad is, however, a major safety measure, as support pad slippage went up to 53% of the pad half-width. These results should challenge 2PAG lift manufacturers and vehicle manufacturers to come up with support pad and lifting point designs, respectively, that would reduce this inherent risk of the 2PAG lifts.

Julius Keller, Flavio Antonio Coimbra Mendonca, Daniel Kwasi Adjekum

An increase in evidence-based studies into the deleterious effects of fatigue on flight operations has been reported by key aviation groups globally. The collegiate aviation flight training environment has not been researched at the same level when compared to military and airline operations. College aged students are unique in the sense that they are tasked with classwork, studying, participation in student organizations, social activities, and often have part time jobs within and outside of the academic environment. These conditions may cause errors, incidents, accidents, poor academic performance, and undesirable health metrics. The purpose of this study was to understand fatigue as a multi-factorial dimension and to assess potential relationships among these factors using hypothesized measurement models. The research team distributed the Collegiate Aviation Fatigue Inventory II (CAFI-II) to eight small, medium, and large collegiate aviation programs in the United States. The CAFI-II primarily focuses on fatigue awareness, causes and symptoms of fatigue, and lifestyle choices. Four hundred and twenty-two (<i>n</i> = 422) valid responses were obtained. Results suggested a direct predictive relationship between fatigue in collegiate flight training and the perceptions of respondents of conditions that are known to cause fatigue. Findings also suggested that respondents who had a favorable perception of fatigue risk and management programs had a better understanding of the causes of fatigue.