Search Results (3,192)

Essential oils are extracted from various parts of plants and have many beneficial properties and applications. These include aromatherapy, healthcare, cosmetics, fragrances, agriculture, household cleaning products, and the food industry. Due to their antimicrobial and antioxidant properties, essential oils are suitable for use as natural flavorings and preservatives, ensuring food quality maintenance and facilitating clean-label product production. In this context, assessing the quality of essential oils is of paramount importance. Among the various analytical methods, electrochemical methods stand out for their simplicity, cost-effectiveness, and environmental friendliness. Consequently, this review examines the applications, advantages, disadvantages, and limitations of electroanalytical methods proposed to quantify major volatile, electroactive components and determine their antioxidant properties. The objective of this evaluation is to establish a framework for future research that will address existing gaps and shortcomings in electroanalytical methodologies. Full article

The aviation industry is adopting liquid hydrogen (LH₂) for sustainable flight, requiring robust safety systems. This work is an example of adaptation of a Micro-Electro-Mechanical Systems (MEMS)-based structural health monitoring (SHM) system for LH₂ tanks, developed in the H2ELIOS project. It uses a multisensor approach that combines MEMS sensors to monitor vibration and acceleration, shape memory alloy (SMA) strain sensors for measuring tank expansion, and hydrogen leakage sensors to prevent false alarms. This SHM technology detects cracks and delamination of material and coating, enabling predictive maintenance via digital twins and ensuring structural integrity. Full article

Centrifugal compressors are critical assets in the oil and gas, petrochemical, and power generation industries, where unplanned downtime results in severe economic and safety consequences. Despite the application of digital twin technology for predictive maintenance, existing approaches struggle to combine accurate degradation modeling with formal assurance evidence that regulators and operators demand before trusting machine learning-augmented systems. This paper proposes a hybrid digital twin framework whose architecture is structured around a formal safety case template, addressing both the accuracy and the trustworthiness challenges simultaneously. The methodology couples a first-principles thermodynamic model with a neural-network residual learner, and the complete system is organized through a design-stage safety case constructed in Goal Structuring Notation. The design stage identifies the requirements for operational deployment. Validation through a simulation study on a one-year synthetic operational dataset shows that the hybrid model reduces root-mean-square prediction error by over 50% for both pressure ratio and polytropic efficiency compared to the physics-only baseline. The anomaly detection module, presented here as a proof of concept, achieves 92% recall in identifying injected faults, and a composite health index tracks the progression of fouling, erosion, and seal wear over the simulated service life. This study is purely theoretical, with no experimental measurements conducted. It demonstrates the structural viability and coherence of the proposed framework within a controlled environment, providing a solid theoretical and computational foundation for future physical validation efforts. These findings provide preliminary evidence that embedding a structured safety argument into the design of a hybrid digital twin is technically feasible and beneficial for building the confidence needed to deploy such systems in safety-critical industrial environments. Full article

To protect human health and the environment, it is necessary to reduce the number of solid particles and harmful gases in the air or to minimize such pollution. Filtration and separation devices are intended for various industrial operations to capture pollutants from various technological processes. In the introduction, this article points out the use of cyclone filters in individual operations, names the most frequently occurring elements of pollution, and suggests the most suitable method of separation. In paint shops, grinding shops, welding workplaces, machining lines, and when handling powder materials, particles with very different properties are created. An important advantage of using cyclone filters is not only their simple construction but also their usability at high temperatures and pressures. Furthermore, this article highlights that cyclones are easy to maintain, typically contain no moving parts, are simple to manufacture, and are cost-effective, particularly as pre-filtration devices. Their efficiency generally ranges from 50% to 99% and is strongly influenced by design and operating parameters, especially cyclone geometry, which affects pressure drop, flow structure, cut diameter, and fractional collection efficiency. The article also summarizes that various modifications of the inlet, vortex finder, outlet pipe, and cyclone body have been proposed to enhance separation performance, particularly for smaller particles. Nevertheless, due to the centrifugal and inertial nature of cyclone separation, fine and submicrometric particulate matter remains difficult to remove using cyclones alone. Fabric filters are also analyzed as a possible solution, but high loading by coarse particles may cause clogging, increased pressure drop, and higher maintenance costs. In the end, the combination of a cyclone with an electrostatic precipitator is presented as a staged separation approach, enabling efficient removal of both coarse particles and fine particulate matter from the gas stream. Full article

This study examines how the Internet of Things (IoT) acts as a key enabler of sustainability in industrial production systems within the Industry 4.0 paradigm, addressing the fragmented understanding of the mechanisms linking digital technologies to environmental, operational, and emerging human-centric outcomes. A systematic literature review was conducted following PRISMA 2020 guidelines using the Web of Science Core Collection. After applying explicit inclusion and exclusion criteria, 69 peer-reviewed studies published between 2016 and 2026 were analyzed through qualitative thematic synthesis and comparative analysis. The findings reveal that IoT functions as a foundational digital infrastructure enabling real-time monitoring, operational transparency, and data-driven decision-making in production environments. Four dominant application domains are identified: (i) energy and resource efficiency, (ii) production monitoring and control, (iii) predictive maintenance and asset management, and (iv) emerging human-centric production systems aligned with Industry 5.0. While IoT consistently improves operational reliability and resource efficiency, its contribution to the social dimension of sustainability remains comparatively underdeveloped. This study advances the existing literature by providing a mechanism-oriented synthesis that explains how IoT-enabled infrastructures generate sustainability outcomes across production systems. Furthermore, it establishes a conceptual bridge between Industry 4.0 digitalization and the transition toward human-centric and resilient manufacturing models associated with Industry 5.0. From a practical perspective, the results highlight that IoT adoption contributes to reducing energy consumption, optimizing resource utilization, and enhancing operational performance, while also supporting safer and more adaptive working environments. However, challenges related to data integration, workforce adaptation, and digital capability gaps persist, underscoring the need for inclusive and strategically aligned digital transformation processes. Full article

Selective laser melting (SLM) equipment operates as a complex cyber–physical system, wherein strong implicit coupling among internal subsystems presents significant challenges for condition monitoring and fault diagnosis. Existing deep learning methods often suffer from feature submersion when processing multi-source heterogeneous data and lack the capability for system-level topological causal inference. To address these issues, we propose a multisystem coupling modeling and anomaly traceability method based on a decoupled graph attention network (ST-DBGAE). Independent local spatiotemporal feature alignment modules are constructed to map heterogeneous sensory data into a unified latent space. This eliminates dimensional discrepancies while strictly maintaining the feature independence of underlying hardware subsystems, such as optical and gas circuits. A dynamic graph attention mechanism with sparse priors is subsequently introduced to adaptively capture time-varying coupling weights triggered by implicit interactions (e.g., thermal fluids), bypassing the need for predefined rigid physical connections. Furthermore, a dual-branch two-stage decoupled optimization architecture is designed. By blocking the cross-interference of global backpropagation, this architecture outputs a continuous equipment health index (HI) based on reconstruction errors and employs a topological difference matrix inference mechanism to reversely anchor the root-cause nodes responsible for cross-system cascading degradation. Experimental results based on over 310,000 real operational monitoring records from industrial SLM equipment demonstrate that the proposed model achieves a comprehensive diagnostic Macro-F1 score of 96.5% across eight operating states. The single-class detection rates (ACCs) of specific underlying anomalies are significantly improved. This method not only enables high-precision equipment health warnings but also provides a physically interpretable microscopic fault propagation mapping for predictive maintenance. Full article

Precision feeding is an important foundation for improving production efficiency in aquaculture, reducing feed waste, mitigating water pollution, and promoting the intelligent development of aquaculture. Conventional feeding practices remain heavily dependent on operator experience and are typically executed at predetermined times or fixed ration levels. Such approaches frequently result in extensive feeding management, poor adaptability, low feed utilization efficiency, and delayed responses to environmental changes. Advances in machine vision, the Internet of Things, machine learning, deep learning, and automatic control have progressively shifted aquaculture feeding research beyond standalone automatic feeders toward integrated systems encompassing demand perception, intelligent decision-making, precise control, and equipment coordination. This paper reviews the state of the art in precision feeding technologies and equipment in aquaculture. At the technical level, it summarizes advances in feeding demand perception, intelligent feeding decision-making, and precise control and execution. At the equipment level, it reviews the main types, design features, and field application status of precision feeding equipment in intensive aquaculture, pond aquaculture, and offshore aquaculture scenarios. Despite the considerable progress achieved, the practical deployment of precision feeding still faces several limitations. Environmental disturbances, water turbidity, illumination variation, and sensor drift may compromise the reliability of feeding demand perception. Existing decision-making models frequently exhibit limited generalizability across species, growth stages, and aquaculture scenarios. Moreover, insufficient integration of sensing, decision-making, and execution restricts the development of fully closed-loop feeding systems. High initial investment, maintenance costs, and the shortage of skilled personnel further constrain the adoption of precision feeding equipment, particularly in resource-limited regions. On this basis, the main challenges including sensing accuracy, model practicability, closed-loop control, equipment reliability, and standardization, are examined. Future development trends are also discussed, covering multi-source information fusion, synergy between mechanistic models and data-driven methods, system-level closed-loop control, equipment modularization, and industrial application. This review is expected to provide a reference for subsequent research and engineering applications. Full article

Industrial control systems (ICS), which manage critical infrastructure such as power grids and water treatment, are increasingly exposed to cyber threats and operational faults as their connectivity to external networks grows. AI-based anomaly detection has emerged as a key defense, yet three limitations restrict its practical deployment: (i) detected anomalies are treated uniformly without distinguishing between transient faults and intentional attacks, hindering tailored incident response; (ii) the trade-off between detection accuracy and the false-positive rate burdens experts with extensive manual triage and delays prompt action; and (iii) prevailing feature-attribution Explainable AI (XAI) techniques such as SHAP and LIME produce fragmented sensor-level explanations and fail to capture correlations among sensors in time-series data, undermining trust in model decisions. To address these gaps, this paper proposes a graph-based deep learning framework that (a) defines anomaly types in terms of the anomalous-sensor ratio measured before and after smoothing—which operationalizes the correlation-maintenance principle that faults keep coupled sensors jointly anomalous while attacks isolate them—enabling explicit separation of faults, attacks, false positives, and false negatives; (b) identifies ambiguous decisions near the detection threshold as candidate false alarms via dynamic threshold smoothing; and (c) provides correlation-aware graph visualizations for intuitive interpretation. Experiments on the Secure Water Treatment (SWaT) dataset center on this post-detection layer: built on a standard graph-based detector (F1-score 0.787 at Top-K = 10) that serves only as the substrate, the categorization separates faults from attacks, and the subsequent ambiguity analysis identifies false negatives with 83% precision and false positives with 73% precision. By separating attacks from faults and surfacing high-likelihood false alarms together with intuitive sensor-correlation explanations, the proposed approach reduces analyst workload and supports more reliable, prioritized incident response in ICS environments. Full article

Multi-source heterogeneous data in discrete manufacturing shop floors, including vibration signals, equipment logs, visual monitoring data, and handwritten production reports, exhibit significant differences in modality and semantic representation. Traditional fusion methods often fail to bridge the semantic gap between low-level sensing signals and high-level manufacturing cognition, limiting intelligent anomaly analysis and decision-making capability. To address this issue, this paper proposes a semantic translation and fusion framework for industrial heterogeneous data based on Knowledge Graph (KG), Retrieval-Augmented Generation (RAG), and Large Language Models (LLMs). First, a unified semantic translation mechanism is developed to convert multimodal industrial data into structured semantic representations for cross-modal alignment. Second, an industrial knowledge graph and RAG mechanism are introduced to integrate process knowledge, maintenance manuals, and historical fault records into the reasoning process. Third, an LLM-driven reasoning framework is designed for multimodal semantic fusion, anomaly identification, causal analysis, and optimization recommendation generation. In addition, a digital twin-based visualization interface is constructed to realize real-time interaction between production lines, industrial data, and intelligent cognitive reports. Experimental results demonstrate that the proposed framework significantly improves industrial reasoning accuracy, anomaly analysis correctness, and response efficiency compared with general-purpose LLMs, providing an effective solution for intelligent cognition and decision-making in discrete manufacturing systems. Full article

This article presents an integrated approach that combines Virtual Reality (VR), Augmented Reality (AR), and an Intelligent Virtual Assistant (IVA) to support training, on-the-job assistance, and maintenance in a textile manufacturing environment. The solution spans three systems: RioRV, a Unity-based VR platform for immersive, step-by-step procedure rehearsal, instructional videos, and simplified 3D animations; RiAR, a mobile AR application for assisted maintenance and access to real-time and historical machine data using marker-based (VuMark) identification; and Ria, a web-based IVA that delivers document-grounded answers, operational queries over a secure plant API, short-horizon forecasting, and a narrow set of guarded remote actions. The architecture prioritizes human-centered Industry 5.0 principles—safety, usability, and resilience—by enabling operators to learn procedures in VR, execute tasks with AR overlays and maintenance media at the workstation, and obtain concise, source-cited guidance via the IVA without leaving immersion. In the case study with a spinning section at RIOPELE, the convergence of VR, AR, and IVA reduced reliance on bulky manuals, shortened time-to-information for machine status, and established a feedback loop in which training and operational experience continuously enrich the knowledge base. Full article

As High-Performance Computing (HPC) architectures evolve towards the Exascale, storage infrastructure reliability has emerged as a critical operational challenge, with traditional reactive and static preventive maintenance strategies proving increasingly insufficient. This study addresses this gap by proposing a comprehensive methodological framework for the transition from predictive to predictive-prescriptive maintenance in large-scale storage environments. By integrating the CRISP-DM industry standard with a multi-layered eXplainable Artificial Intelligence (XAI) suite, we develop a system capable of isolating hardware degradation signals amidst massive volumes of routine telemetry. To validate our approach, we leveraged a publicly available disk failure dataset to evaluate multiple Machine Learning configurations, addressing the challenge of severe class imbalance through optimized oversampling and Gradient Boosting algorithms. The methodology employs global and local XAI techniques, including Permutation Feature Importance, SHAP, and surrogate decision trees, to translate probabilistic risk assessments into auditable hardware engineering rules. Our results demonstrate that this hybridization of robust predictive modeling with multi-layered explainability provides a transparent, evidence-based decision support system. Ultimately, we conclude that converting opaque risk predictions into technical justifications enables infrastructure managers to optimize hardware lifecycle management and minimize system downtime in mission-critical environments, establishing a viable pathway toward more resilient and auditable storage management. Full article

Optical remote sensing image object detection is widely applied in industrial monitoring, infrastructure maintenance, and intelligent scheduling, yet its accuracy is limited by complex background interference, dense multi-scale object distributions, and loss of small object details. To address these challenges, this paper proposes the ORSSO-DETR model based on RT-DETR, which improves the hybrid encoder’s ability to adapt to the characteristics of optical remote sensing images. The proposed method integrates the C3K2 multi-scale fusion module with the FCM feature complementary mapping module to enhance feature fusion, introduces a dynamic convolution-based DynamicConv downsampling module and the EUCB upsampling technique to improve multi-scale feature retention and texture detail preservation, optimizes the encoder structure by preprocessing high-level features with 1 × 1 convolution, and replaces the original normalization layer in the Transformer with the Dynamic Tanh module to strengthen nonlinear expressive capability. Experimental results show that the proposed method achieves 96.2% mAP@50 and 64.4% mAP@50:95 on the NWPU VHR-10 dataset, improving by 3.9% and 2.9% over the baseline, respectively. On the RSOD dataset, it achieves 97.6% mAP@50 and 71.2% mAP@50:95, with improvements of 2.5% and 2.8%, respectively. These results validate the effectiveness of the proposed method in improving small object detection accuracy in optical remote sensing images. Full article

Reliable bearing fault diagnosis in induction motors is essential for predictive maintenance and Industrial Internet of Things (IIoT) applications. However, diagnostic models that perform well under random or measurement-wise data splits may fail when deployed under unseen operating conditions. This study presents a robustness-oriented comparative evaluation of induction motor bearing fault diagnosis models using vibration and phase-current signals from a controlled medium subset of the Paderborn bearing dataset. Raw temporal 1D-CNN models, STFT-based 2D-CNN representations, and vibration–current fusion strategies were evaluated under measurement-wise and operating-condition holdout protocols. Under measurement-wise validation, the 1D-CNN Early Fusion model achieved a Macro-F1 score of 0.9251. Under the stricter operating-condition holdout setting, the same model achieved the highest robustness among the evaluated CNN models. Multi-seed validation confirmed its stability, with a mean Macro-F1 of 0.8626, a worst-case Macro-F1 of 0.7159, and a robustness score of 0.7850. The selected model remained lightweight, requiring 73,891 trainable parameters and an estimated model size of 0.282 MB. Additional revision experiments were conducted to address bearing-identity sharing and classical baseline comparisons. In the bearing-code-disjoint validation test, both raw temporal models showed reduced performance, and early fusion did not significantly outperform vibration-only learning. The 1D-CNN Vibration model achieved a mean Macro-F1 of 0.5616, while the 1D-CNN Early Fusion model achieved 0.5485; the paired Wilcoxon test was not significant (p = 0.2016). Classical baselines using handcrafted time-domain, frequency-domain, envelope-inspired, and spectral-kurtosis features were also evaluated. The strongest classical baseline, vibration-feature XGBoost, achieved a mean Macro-F1 of 0.8582 under condition-holdout validation. Overall, the findings show that lightweight vibration–current early fusion provides a favorable robustness–complexity trade-off under operating-condition shift. However, the bearing-code-disjoint results indicate that complete generalization to unseen bearing identities remains challenging. Therefore, the deployment claims are limited to computational feasibility indicators, and further validation on embedded hardware, additional datasets, and stricter cross-domain protocols is required. Full article

While rapid digital transformation has significantly optimized sectors such as finance and e-commerce, maintenance management in industrial environments has historically received lower levels of technological and capital investment. This lag creates critical gaps in operational efficiency and asset longevity, particularly within renewable energy infrastructures where sustainability and resilience are paramount. Addressing this technological disparity is essential for minimizing ecological footprints and maximizing the viability of net-zero systems. This paper introduces an advanced multi-platform digital solution designed to optimize the operation and maintenance of renewable energy systems and smart infrastructures. The platform addresses traditional management gaps by implementing standardized protocols that integrate real-time remote monitoring, sensor networks, and cloud-based data acquisition. By centralizing historical and real-time data from solar, wind, and hybrid grids, it facilitates advanced analytics, such as predictive modeling of component degradation. Real-world validation across photovoltaic plants and wind farms demonstrates significant impacts: a 30% reduction in unplanned outages and a 20% to 25% decrease in operational and maintenance costs. The results confirm that digitalizing maintenance processes is a strategic pillar for the energy transition, aligning industrial performance with global low-carbon pathways. Full article

AI-enabled quality control (AI-QC) tools are increasingly available to manufacturing SMEs in emerging economies, yet the firm-level conditions associated with their adoption remain underexamined. Building on the Technology–Organization–Environment (TOE) framework of Tornatzky and Fleischer, integrated with the perceived usefulness and perceived ease-of-use constructs of the Technology Acceptance Model (TAM), this study examines the determinants of AI-QC adoption intention, and its association with operational performance improvement, in 284 manufacturing SMEs from Turkey, Malaysia, and Egypt. The focal dependent construct is adoption intention rather than realized adoption. The AI-QC technologies considered are machine learning defect detection, computer vision inspection, predictive maintenance, and digital twin integration. Three complementary analytical procedures are applied to the same data: partial least squares structural equation modeling (PLS-SEM) to estimate the strength of the modeled associations, importance–performance map analysis (IPMA) to identify high-importance but low-performance predictors, and fuzzy-set qualitative comparative analysis (fsQCA) to identify combinations of conditions jointly sufficient for high adoption intention. The PLS-SEM estimates indicate positive associations for the technological, organizational, and environmental predictors, with top management support, perceived usefulness, and organizational readiness showing the largest coefficients and data security concern showing a negative association; effect magnitudes varied considerably, and several were small. The IPMA results indicate that the two most important predictors exhibit comparatively low performance scores in the sample. The fsQCA results identify three configurations associated with high adoption intention. Because the design is cross-sectional and based on self-reported, single-respondent data, the findings are interpreted as associations rather than causal effects. The paper concludes with guidance for SME managers, AI technology vendors, and industrial policymakers. Full article