Hasil untuk "Industrial safety. Industrial accident prevention"

Г.З. Файнбург , Л.В. Михайловская

Mykola Dumenko

Мета статті. Розробити методику оцінювання можливостей системи кадрових органів Збройних Сил України щодо виконання завдань у воєнний час. Методи дослідження. У статті розглядаються три взаємопов’язані методи. Метод обґрунтування складу системи кадрових органів Збройних Сил України (n), який базується на математичній моделі системи кадрових органів, що являє собою мережу масового обслуговування та передбачає вирішення оптимізаційної задачі знаходження оптимальної чисельності кадрових органів, які задовольняють різним критеріям функціонування системи кадрових органів Збройних Сил України та дає змогу отримати: детальну ієрархічну модель системи кадрових органів Збройних Сил України з визначеним рівнем декомпозиції та відобразити наочно взаємозв’язок між кадровими органами; проаналізувати систему кадрових органів Збройних Сил України та обґрунтувати раціональний варіант її побудови залежно від заданої ймовірності виконання кадрових завдань. Метод оптимального розподілу людських ресурсів між військовими формуваннями ( ) враховує темпи мобілізаційного розгортання, обсяги надходження мобілізаційного людського ресурсу та безповоротні й санітарні втрати під час мобілізації і бойових дій. Він ґрунтується на оптимізації рішень шляхом вирішення багатокрокової задачі оптимізації розподілу людських ресурсів (метод динамічного програмування) на основі даних щодо комплектування військових формувань у попередніх періодах комплектування та вимог до укомплектованості їх у  послідуючі періоди, що дає можливість знайти оптимальний розподіл людських ресурсів між військовими формуваннями і забезпечить досягнення максимальної їх укомплектованості на кінець визначеного терміну комплектування. Метод оцінювання ефективності функціонування системи кадрових органів Збройних Сил України дав змогу оцінити ефективність функціонування цієї системи з урахуванням інтенсивності надходження кадрових завдань та раціонального складу кадрових органів і розрахувати загальний показник ефективності функціонування системи, здійснити зважену адитивну згортку показників часткової ефективності, які розраховані на основі показників оперативності, якості забезпечення та ефективності структури й організації роботи системи кадрових органів. Для визначення ступеня важливості завдань застосовано метод аналізу ієрархій, який дає змогу оперативно оцінити ефективність функціонування системи кадрових органів для подальшого прийняття управлінських рішень щодо її удосконалення (W ско). Отримані результати дослідження. Запропонована методика дає змогу комплексно врахувати часткові показники ефективності системи кадрових органів, які характеризують якість окремих її властивостей. Водночас, прогнозування укомплектованості органів військового управління, військових частин і підрозділів, а також оцінювання впливів зазначених складових на ефективність системи кадрових органів надає можливість досліджувати вплив окремих її елементів на загальний рівень ефективності. Це, в свою чергу, дасть змогу оцінити діяльність системи на підставі аналізу структури і змісту роботи підрозділів системи кадрових органів й системи у цілому, як на етапі їх функціонування у мирний час, так і планування їх розвитку на період воєнного стану. Елементи наукової новизни. Використання запропонованого методичного апарату оцінювання ефективності функціонування системи кадрових органів Збройних Сил України дало змогу розробити методику оцінювання можливостей системи кадрових органів Збройних Сил України щодо виконання завдань у воєнний час. Теоретичне й практичне значення викладеного у статті. Методика передбачає: оцінювання показників ефективності системи кадрових органів; аналіз та комплексування показників оцінювання ефективності системи кадрових органів для різних варіантів її побудови, їх перевірку за відповідними критеріями; вироблення пропозицій щодо вибору раціональної структури системи кадрових органів на підставі аналізу значень вказаних показників та перевірки критеріїв для різних варіантів формування системи кадрових органів у воєнний час. Практична значущість: під час розгляду варіантів замислу операції – використовувати розроблену методику для оптимального розподілу людських ресурсів між військовими формуваннями стосовно кожного варіанту, що розглядається, замислу застосування військ; під час розрахунку потрібного складу чисельності кадрових органів Збройних Сил України – використовувати запропоновану методику для укомплектування військ (сил) з урахуванням прогнозованих втрат особового складу, які виникатимуть в ході операції (бойових дій) як унаслідок впливу противника, так і недостатньої кількості особового складу кадрових органів та оцінити можливості існуючого складу системи кадрових органів та її вплив на рівень боєздатності військ; під час підготовки необхідних даних для директивних і планових документів – спиратися на результати проведеного обґрунтування складу та чисельності системи кадрових органів Збройних Сил України; в процесі оцінювання ефективності функціонування системи кадрових органів Збройних Сил України; під час підготовки пропозицій командуванню щодо укомплектованості військ (сил) у визначені періоди та можливості системи кадрових органів Збройних Сил України з перерозподілу наявних людських ресурсів.

Changheon Han, Yuseop Sim, Hoin Jung et al.

Acoustic signals from industrial machines offer valuable insights for anomaly detection, predictive maintenance, and operational efficiency enhancement. However, existing task-specific, supervised learning methods often scale poorly and fail to generalize across diverse industrial scenarios, whose acoustic characteristics are distinct from general audio. Furthermore, the scarcity of accessible, large-scale datasets and pretrained models tailored for industrial audio impedes community-driven research and benchmarking. To address these challenges, we introduce DINOS (Diverse INdustrial Operation Sounds), a large-scale open-access dataset. DINOS comprises over 74,149 audio samples (exceeding 1,093 hours) collected from various industrial acoustic scenarios. We also present IMPACT (Industrial Machine Perception via Acoustic Cognitive Transformer), a novel foundation model for industrial machine sound analysis. IMPACT is pretrained on DINOS in a self-supervised manner. By jointly optimizing utterance and frame-level losses, it captures both global semantics and fine-grained temporal structures. This makes its representations suitable for efficient fine-tuning on various industrial downstream tasks with minimal labeled data. Comprehensive benchmarking across 30 distinct downstream tasks (spanning four machine types) demonstrates that IMPACT outperforms existing models on 24 tasks, establishing its superior effectiveness and robustness, while providing a new performance benchmark for future research.

Aaditya Baranwal, Abdul Mueez, Jason Voelker et al.

Large-scale Vision-Language Models (VLMs) have transformed general-purpose visual recognition through strong zero-shot capabilities. However, their performance degrades significantly in niche, safety-critical domains such as industrial spill detection, where hazardous events are rare, sensitive, and difficult to annotate. This scarcity -- driven by privacy concerns, data sensitivity, and the infrequency of real incidents -- renders conventional fine-tuning of detectors infeasible for most industrial settings. We address this challenge by introducing a scalable framework centered on a high-quality synthetic data generation pipeline. We demonstrate that this synthetic corpus enables effective Parameter-Efficient Fine-Tuning (PEFT) of VLMs and substantially boosts the performance of state-of-the-art object detectors such as YOLO and DETR. Notably, in the absence of synthetic data (SynSpill dataset), VLMs still generalize better to unseen spill scenarios than these detectors. When SynSpill is used, both VLMs and detectors achieve marked improvements, with their performance becoming comparable. Our results underscore that high-fidelity synthetic data is a powerful means to bridge the domain gap in safety-critical applications. The combination of synthetic generation and lightweight adaptation offers a cost-effective, scalable pathway for deploying vision systems in industrial environments where real data is scarce/impractical to obtain. Project Page: https://synspill.vercel.app

Francesco Aurelio Pironti, Angelo Furfaro, Francesco Blefari et al.

The security of Industrial Control Systems (ICSs) is critical to ensuring the safety of industrial processes and personnel. The rapid adoption of Industrial Internet of Things (IIoT) technologies has expanded system functionality but also increased the attack surface, exposing ICSs to a growing range of cyber threats. Honeypots provide a means to detect and analyze such threats by emulating target systems and capturing attacker behavior. However, traditional ICS honeypots, often limited to software-based simulations of a single Programmable Logic Controller (PLC), lack the realism required to engage sophisticated adversaries. In this work, we introduce a modular honeynet framework named ICSLure. The framework has been designed to emulate realistic ICS environments. Our approach integrates physical PLCs interacting with live data sources via industrial protocols such as Modbus and Profinet RTU, along with virtualized network components including routers, switches, and Remote Terminal Units (RTUs). The system incorporates comprehensive monitoring capabilities to collect detailed logs of attacker interactions. We demonstrate that our framework enables coherent and high-fidelity emulation of real-world industrial plants. This high-interaction environment significantly enhances the quality of threat data collected and supports advanced analysis of ICS-specific attack strategies, contributing to more effective detection and mitigation techniques.

Antoine Houssard

In the field of artificial intelligence (AI) research, there seems to be a rapprochement between academics and industrial forces. The aim of this study is to assess whether and to what extent industrial domination in the field as well as the ever more frequent switch between academia and industry resulted in the adoption of industrial norms and practices by academics. Using bibliometric information and data on scientific code, we aimed to understand academic and industrial researchers' practices, the way of choosing, investing, and succeeding across multiple and concurrent artifacts. Our results show that, although both actors write papers and code, their practices and the norms guiding them differ greatly. Nevertheless, it appears that the presence of industrials in academic studies leads to practices leaning toward the industrial side, but also to greater success in both artifacts, suggesting that if convergence is, then it is passing through those mixed teams rather than through pure academic or industrial studies.

Abdelrahman Alzarooni, Ehtesham Iqbal, Samee Ullah Khan et al.

Anomaly detection from images captured using camera sensors is one of the mainstream applications at the industrial level. Particularly, it maintains the quality and optimizes the efficiency in production processes across diverse industrial tasks, including advanced manufacturing and aerospace engineering. Traditional anomaly detection workflow is based on a manual inspection by human operators, which is a tedious task. Advances in intelligent automated inspection systems have revolutionized the Industrial Anomaly Detection (IAD) process. Recent vision-based approaches can automatically extract, process, and interpret features using computer vision and align with the goals of automation in industrial operations. In light of the shift in inspection methodologies, this survey reviews studies published since 2019, with a specific focus on vision-based anomaly detection. The components of an IAD pipeline that are overlooked in existing surveys are presented, including areas related to data acquisition, preprocessing, learning mechanisms, and evaluation. In addition to the collected publications, several scientific and industry-related challenges and their perspective solutions are highlighted. Popular and relevant industrial datasets are also summarized, providing further insight into inspection applications. Finally, future directions of vision-based IAD are discussed, offering researchers insight into the state-of-the-art of industrial inspection.

Karina M. Sindermann, Esma S. Gel, Nesim K. Erkip

Industrial Vending Machines (IVMs) automate the dispensing of a variety of supplies like safety equipment and tools at customer sites, providing 24/7 access while tracking inventory in real-time. Industrial distribution companies typically manage the replenishment of IVMs using periodic schedules, which do not take advantage of these advanced real-time monitoring capabilities. We develop two approaches to optimize the long-term average cost of replenishments and stockouts per unit time: a state-dependent optimal control policy that jointly considers all inventory levels (referred to as trigger set policy) and a fixed cycle policy that optimizes replenishment frequency. We prove the monotonicity of the optimal trigger set policy and leverage it to design a computationally efficient approximate online control framework. Unlike existing methods, which typically handle a very limited number of items due to computational constraints, our approach scales to hundreds of items while achieving near-optimal performance. Leveraging transaction data from our industrial partner, we conduct an extensive set of numerical experiments to demonstrate this claim. Our results show that optimal fixed cycle replenishment reduces costs by 61.7 to 78.6% compared to current practice, with our online control framework delivering an additional 4.1 to 22.9% improvement. Our novel theoretical results provide practical tools for effective replenishment management in this modern vendor-managed inventory context.

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.

Ahmed Jalil Al-Bayati, Mason Ali, Fadi Alhomaidat et al.

The safety of construction and maintenance work zones has been highlighted as a crucial aspect of construction management that requires special attention due to the increasing number of fatal and non-fatal injuries in recent years. Temporary traffic control (TTC) is required by the Occupational Safety and Health Administration (OSHA) to improve overall safety performance during road construction and maintenance projects. The fact that speeding and distracted drivers may overlook TTC warning signs and directions has been reported as one of the leading causes of work zone incidents. This study aimed to examine both the impact of temporary portable rumble strips (TPRSs) on traffic speeds and the response of different vehicle types in road work zones, including trucks and cars. Accordingly, field experiments were conducted in collaboration with the Road Commission for Oakland County (RCOC) in Michigan. The findings indicate that TPRSs have a statistically significant impact on the driving speed of light vehicle drivers but not on medium and heavy vehicles, such as trucks. This study contributes to the existing literature by quantifying the safety benefits of TPRS use, providing valuable data for policymakers and construction professionals. By demonstrating the effectiveness of TPRSs in reducing the speed of light vehicles, this research supports the implementation of these systems as a practical measure for enhancing safety within road construction work zones. Additionally, this study highlights the need for tailored approaches to address the limited impact on larger vehicles, underscoring the importance of developing complementary strategies to ensure comprehensive safety improvements across all vehicle types.

Crystal May BROWN, Rene OOSTHUIZEN, Roman TANDLICH

Adaptation and changes to the management of infectious diseases result from human research, knowledge gathering, interpretation and applications. Findings from the current study clearly point to the nature of the policy and disaster risk management response to COVID19, as having characteristics of a super-wicked problem. This provides an explanation for the sometimes diverging strategies in tackling the impacts of the coronavirus pandemic on humans and the surrounding socio-ecological systems. Solutions to the pandemic and its long-term outcomes will have to take into account the disparity of impacts and pre-disaster conditions.

Vivek Chavan, Oliver Heimann, Jörg Krüger

Egocentric vision aims to capture and analyse the world from the first-person perspective. We explore the possibilities for egocentric wearable devices to improve and enhance industrial use cases w.r.t. data collection, annotation, labelling and downstream applications. This would contribute to easier data collection and allow users to provide additional context. We envision that this approach could serve as a supplement to the traditional industrial Machine Vision workflow. Code, Dataset and related resources will be available at: https://github.com/Vivek9Chavan/EgoVis24

Carmine Cesarano, Roberto Natella

Application Layer Gateways (ALGs) play a crucial role in securing critical systems, including railways, industrial automation, and defense applications, by segmenting networks at different levels of criticality. However, they require rigorous security testing to prevent software vulnerabilities, not only at the network level but also at the application layer (e.g., deep traffic inspection components). This paper presents a vulnerability-driven methodology for the comprehensive security testing of ALGs. We present the methodology in the context of an industrial case study in the railways domain, and a simulation-based testing environment to support the methodology.

Lei Ren, Haiteng Wang, Jinwang Li et al.

With the remarkable success of generative models like ChatGPT, Artificial Intelligence Generated Content (AIGC) is undergoing explosive development. Not limited to text and images, generative models can generate industrial time series data, addressing challenges such as the difficulty of data collection and data annotation. Due to their outstanding generation ability, they have been widely used in Internet of Things, metaverse, and cyber-physical-social systems to enhance the efficiency of industrial production. In this paper, we present a comprehensive overview of generative models for industrial time series from deep generative models (DGMs) to large generative models (LGMs). First, a DGM-based AIGC framework is proposed for industrial time series generation. Within this framework, we survey advanced industrial DGMs and present a multi-perspective categorization. Furthermore, we systematically analyze the critical technologies required to construct industrial LGMs from four aspects: large-scale industrial dataset, LGMs architecture for complex industrial characteristics, self-supervised training for industrial time series, and fine-tuning of industrial downstream tasks. Finally, we conclude the challenges and future directions to enable the development of generative models in industry.

Dimitrios Kouzapas, Christos G. Panayiotou, Demetrios G. Eliades

Modern industrial systems require frequent updates to their cyber and physical infrastructures, often demanding considerable reconfiguration effort. This paper introduces the industrial Cyber-Physical Systems Description Language, iCPS-DL, which enables autonomic reconfigurations for industrial Cyber-Physical Systems. The iCPS-DL maps an industrial process using semantics for physical and cyber-physical components, a state estimation model, and agent interactions. A novel aspect is using communication semantics to ensure live interaction among distributed agents. Reasoning on the semantic description facilitates the configuration of the industrial process control loop. A Water Distribution Networks domain case study demonstrates iCPS-DL's application.

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.

Putri Ayuni Alayyannur, Shintia Yunita Arini, Dani Nasirul Haqi et al.

Introduction: Amidst the COVID-19 pandemic, nearly all non-critical sectors require their workers to work remotely, including lecturers. All teaching and learning activities are carried out online. During this period, the problem of psychosocial disorders is of particular concern. Therefore, a study is deemed necessary to analyze what factors contribute to job stress in lecturers working from home during the pandemic. Methods: A cross-sectional study was conducted with a total 0f 111 respondents. This study was conducted through an online survey. The population of this study was lecturers. All data were collected in 2021. Variables in this study were sex, age, working time per day, break time per day, sleep time per day, workout time per week, circadian rhythm, insomnia and work stress. Results: The results of this study indicate that there is no relationship between sex and job stress also a very weak relationship strength between age, sleep time per day, workout time per week, circadian rhythm, insomnia and job stress. Furthermore, there is a strong, unidirectional relationship between working time and job stress level and a weak relationship between break time per day and job stress level. Conclusion: The conclusion is that all variables except sex have a relationship with job stress but with varying degrees. Further research on this study in different population and different methods is suggested.

Mairi Bowdler, Wouter Martinus Petrus Steijn, Dolf van der Beek

For years, the connection between safety behaviours and injury and illness in high-risk industries has been recognised, but the effectiveness of this link has been somewhat overlooked. Since there is still a significant amount of injury within high-risk workplaces, this systematic review aims to examine the effectiveness of behavioural interventions to decrease fatal and non-fatal injuries within high-risk industries. Scopus and Google Scholar were used to find relevant systematic reviews and meta-analyses on this topic. In total, 19 articles met the inclusion criteria. Of these articles, 11 suggested that their reviewed interventions revealed some evidence of being effective in reducing injury/accident rates. Additionally, seven of the papers found that the interventions affected certain determinants, such as safety knowledge, health and safety behaviours, attitudes, efficacy, and beliefs. One of the papers found no effect at all. It must be noted that a significant amount of the articles (<i>n</i> = 10) reported methodological quality or quantity issues, implying that the results should be approached with caution. Nonetheless, it was found that certain components, such as multi-faceted interventions tailored to the target group, contribute to either reducing injury/accident rates or improving the specific aforementioned determinants. There is a need for additional safety interventions in high-risk industries that are based on methodologically sound structural elements and theoretical frameworks. Existing approaches, such as Intervention Mapping, can assist safety professionals in achieving this goal.

Jiancheng Zhao, Jiaqi Yue, Liangjun Feng et al.

Fault diagnosis is a critical aspect of industrial safety, and supervised industrial fault diagnosis has been extensively researched. However, obtaining fault samples of all categories for model training can be challenging due to cost and safety concerns. As a result, the generalized zero-shot industrial fault diagnosis has gained attention as it aims to diagnose both seen and unseen faults. Nevertheless, the lack of unseen fault data for training poses a challenging domain shift problem (DSP), where unseen faults are often identified as seen faults. In this article, we propose a knowledge space sharing (KSS) model to address the DSP in the generalized zero-shot industrial fault diagnosis task. The KSS model includes a generation mechanism (KSS-G) and a discrimination mechanism (KSS-D). KSS-G generates samples for rare faults by recombining transferable attribute features extracted from seen samples under the guidance of auxiliary knowledge. KSS-D is trained in a supervised way with the help of generated samples, which aims to address the DSP by modeling seen categories in the knowledge space. KSS-D avoids misclassifying rare faults as seen faults and identifies seen fault samples. We conduct generalized zero-shot diagnosis experiments on the benchmark Tennessee-Eastman process, and our results show that our approach outperforms state-of-the-art methods for the generalized zero-shot industrial fault diagnosis problem.

Amir Eshaghi Chaleshtori, Abdollah aghaie

In the Engineering discipline, predictive maintenance techniques play an essential role in improving system safety and reliability of industrial machines. Due to the adoption of crucial and emerging detection techniques and big data analytics tools, data fusion approaches are gaining popularity. This article thoroughly reviews the recent progress of data fusion techniques in predictive maintenance, focusing on their applications in machinery fault diagnosis. In this review, the primary objective is to classify existing literature and to report the latest research and directions to help researchers and professionals to acquire a clear understanding of the thematic area. This paper first summarizes fundamental data-fusion strategies for fault diagnosis. Then, a comprehensive investigation of the different levels of data fusion was conducted on fault diagnosis of industrial machines. In conclusion, a discussion of data fusion-based fault diagnosis challenges, opportunities, and future trends are presented.