Search Results (925)

Real-time detection of chemical warfare agents (CWAs) and toxic industrial chemicals underpins military protection, counter-terrorism, and emergency response. Yet field instruments usually fail for a reason unrelated to sensitivity: they cannot identify agents that are not already in their reference libraries, such as novel analogs, mixtures, and degradation products. We argue that this unknown-agent problem is a structural limitation of single-modality sensing, because any one class of information (molecular bonds, ion mobility, elemental composition, or chemical reactivity) is rarely sufficient to resolve an unfamiliar threat. We review the dominant field modalities, including FTIR, Raman/SERS, ion mobility and field-asymmetric ion mobility spectrometry, laser- and spark-induced plasma spectroscopy, metal-oxide sensor arrays, and portable mass spectrometry, and show that their weaknesses are largely complementary. We then set out the principle of orthogonal multimodal sensing, in which complementary information axes are combined by machine learning with anomaly and open-set detection so that unfamiliar agents are recognized as such rather than misidentified. Four hybrid architectures are critically compared, and we examine spark-induced decomposition diagnostics, consumable-free self-decontaminating field systems with edge AI, and the open challenges of standardized datasets, calibration transfer, and validation, before outlining a roadmap toward field-relevant recognition of unidentified chemical threats. Full article

With the rapid development of Industrial Internet of Things (IIoT), large amounts of sensor data generated by industrial devices and edge nodes have become the basis of intelligent manufacturing applications. Collaborative modeling on these distributed data is important for tasks such as anomaly detection, equipment monitoring, and predictive maintenance. Federated learning offers a practical way to train models without exposing raw sensor data, but it still faces privacy leakage and malicious poisoning attacks. To address these issues, this paper proposes a hierarchical privacy protection and poisoning-robust defense framework for industrial federated learning. Starting from the sensitivity differences among parameters at different model layers, the proposed method designs a hierarchical privacy-budget allocation strategy that enhances protection for sensitive information while minimizing the performance impact of perturbation. Meanwhile, a multi-layer, multi-feature anomaly-detection mechanism is adopted to identify malicious updates by jointly exploiting directional consistency, scale stability, and inter-layer similarity, and majority voting together with update clipping is used to further improve system robustness. Experiments on Fashion-MNIST, MVTec AD, and C-MAPSS demonstrate that the proposed method can effectively suppress global-model degradation under multiple poisoning attacks and achieves a favorable balance among privacy protection strength, robustness, and training efficiency. Full article

Wheat flour transportation involves extended transit periods, which present considerable challenges for safety risk oversight. Therefore, it is essential to develop an efficient anomaly detection method to support risk assessment during this stage. However, existing anomaly detection methods often neglect the coupling effects across different time scales and the spatial clustering of hazard factors. To address this limitation, we propose a multi-scale spatial–temporal graph model-based unsupervised anomaly detection framework (MSTUAD), which can simultaneously capture the spatial–temporal correlations between importance and hazard factors across multiple time scales. Specifically, feature maps are first constructed for each hazard factor within a given time window to represent coupling effects. Secondly, a multi-scale spatial–temporal graph model is designed to extract spatial–temporal characteristics from these feature maps. Finally, a reconstruction model based on a variational autoencoder learns latent representations of spatial–temporal features of hazard factors, thereby capturing the intrinsic characteristics of normal wheat flour. Experimental validation on the wheat flour transportation hazard factor dataset and three public industrial datasets demonstrates that MSTUAD significantly outperforms state-of-the-art anomaly detection methods, achieving an average F1-score greater than 84.55%. This approach provides valuable decision support and technical guidance for relevant regulatory authorities. Full article

With the ever-evolving and constantly growing need for electric vehicles (EVs) in the automotive industry, the importance of reliable, efficient, and dynamic EV chargers is increasing. Power electronics is the enabler and forms the execution layer of any charging station. EV charging must now integrate fast control, wide adaptability, bidirectionality, and reliability. The existing literature provides overview studies on the EV charging system, its infrastructure, and necessary power electronics. However, the integration of 5G-enabled EV charging (i.e., data transmission/communication via the 5G network) has introduced new performance expectations that go beyond the capabilities of conventional power converters. This paper presents an in-depth overview of power electronics applications in a 5G-enabled EV charging system. Several key aspects such as advanced converter topologies, a resonant converter for 5G charging, the integration of power electronics with 5G communication, and anomaly and intrusion detection models for a 5G-enabled Blink-2-level EV charger are discussed. Moreover, the challenges and risks of integrating 5G into EV charging infrastructure are discussed. Some recommendations on future research opportunities are provided. This study provides a basic guideline on power electronics applications in a 5G-enabled EV charging system and is therefore valuable to the researchers, scientists, and engineers working in this interesting field. Full article

Retrieval-augmented large language model (LLM) post-processing reportedly improves anomaly triage over streaming industrial Internet of Things (IoT) sensor data, yet its LLM and retrieval contributions are rarely separated. We audit a build-verified pipeline (detector, LLM, retrieval, reranking), adding each stage at a matched detector recall of 0.9 under SHA-256-frozen preregistration, with bootstrap intervals and two generator sizes on 40 NAB and SKAB streams (19 industrial). Retrieval, the preregistered primary contrast, is null in seven of eight design configurations, but a preregistered grid of lower recall targets breaks that null on precision at both targets under stream-level resampling and in two of four cells when benchmark families are the unit. Across that grid, the LLM gain (72B F1 +0.0502) shrinks as candidate recall falls (+0.0227 at 0.7, undetectable at 0.5), which is a bound, not a dose–response curve. There, retrieval buys precision at a significant cost of recall. On the 19 industrial streams, the LLM gain is null at one label-free point and exactly zero at the other two, where the generator confirmed every candidate. Faithfulness, by local natural language inference, is low in every arm and not raised by retrieval. Three annotators labeling 133 claims (Fleiss’ κ = 0.555) placed the shortfall in the verdicts; none entailed by consensus. The protocol is released as a tested artifact. Full article

The digitalization of process industries via the Industrial Internet of Things (IIoT) has introduced challenges like poor generalization, lack of interpretability, and complex multi-frequency sensor data. Most existing systems struggle to simultaneously detect anomalies and trace their root causes, resulting in over-specialized, inflexible solutions. To address this, we present a generic framework for multivariate anomaly detection and root-cause analysis (RCA) featuring dual detection pathways. The Slow Anomaly Detection System (SADS) blends physics-based models with a data-driven LSTM to detect gradual anomalies in the absolute domain. Meanwhile, the Fast Anomaly Detection System (FADS) identifies abrupt deviations in the derivative domain. When anomalies occur, an RCA module identifies the probable input cause using model-weighted deviation scoring, which amplifies baseline deviations by their target sensitivity. Validated on an operational Reverse Osmosis (RO) plant dataset using synthetic anomaly injections, SADS showed high precision for severe anomalies, and both systems achieved strong AUC scores of 0.85–1.00. The framework is designed for transferability across continuous process industries through its modular architecture, with the physics-based component requiring the most substantial process-specific redevelopment; validation beyond the RO case study presented here is identified as a priority direction for establishing this transferability empirically. Full article

Industrial anomaly detection is commonly performed under one-class conditions because defective samples are scarce or unavailable during training. This study presents a lightweight one-class anomaly detection framework that reduces the dependence of compact deep representations on a single arbitrarily selected feature subset. Intermediate features extracted from a frozen MobileNetV2 backbone are divided into five independently sampled 100-channel subspaces. A position-specific diagonal Gaussian model is fitted separately to each subspace using only normal training images. The resulting anomaly scores are robustly normalized and combined through a normal-calibrated reliability-weighting strategy, while image-level decisions are obtained from the highest-scoring local patches. To prevent information leakage, operating thresholds are determined through five-fold cross-fitted calibration using only normal training scores. The proposed compact random-subspace ensemble was evaluated on all 15 categories of MVTec AD and externally validated on the 12-category VisA benchmark. On MVTec AD, the ensemble increased the macro ROC-AUC from 0.9350 to 0.9437 and the macro F1 score from 0.8366 to 0.8504 compared with a single random 100-channel subspace. External validation on VisA showed the same improvement trend, with higher macro ROC-AUC, AP, and F1 values than the single-subspace configuration. The results demonstrate that combining multiple compact normal-data subspaces improves discrimination and reduces reliance on the evaluated single random representation while retaining a lightweight, anomaly-label-free training procedure. Full article

Video Anomaly Detection (VAD) has emerged as a promising technology for automated surveillance and industrial monitoring. However, selecting an appropriate VAD architecture for real-world deployment remains challenging because benchmark-oriented evaluations often overlook practical considerations such as computational efficiency, latency, maintainability, supervision requirements, and long-term operational stability. To address this gap, this study proposes an expert-driven Multi-Criteria Decision Analysis (MCDA) framework for the deployment-oriented selection of VAD architectures. Eight representative architecture families were evaluated against six industrially relevant criteria, with scenario-specific priorities derived using the Best–Worst Method (BWM) from five domain experts across four representative industrial deployment scenarios. Edge-oriented architectures ranked first in three scenarios, achieving MCDA scores of 4.26, 3.90, and 4.07, whereas lightweight CNN-based architectures achieved the highest score (4.12) in the resource-constrained scenario. Inter-expert agreement ranged from Kendall’s W = 0.54 to 0.85, and Monte Carlo analysis confirmed the robustness of rankings, with top-rank probabilities of 69–74% for edge-oriented architectures and 100% for lightweight CNNs in the resource-constrained scenario. These findings demonstrate that architectural suitability depends on the deployment context rather than on a universally superior modeling paradigm, and that industrial VAD should be approached as a deployment-oriented systems engineering problem. The proposed framework provides a transparent and robust basis for aligning VAD architecture selection with operational requirements. Full article

The rapid deployment of Internet of Things (IoT) devices across smart cities, healthcare systems, industrial automation, transportation networks, smart grids, and cyber-physical infrastructures has expanded the modern cyberattack surface. IoT devices are often constrained by limited processing capacity, memory, battery power, and communication bandwidth, making conventional security mechanisms difficult to deploy consistently at scale. Intrusion detection systems (IDSs) provide an important defensive layer; however, many machine-learning-based IDSs are developed under static assumptions and may experience performance degradation as traffic distributions evolve due to firmware changes, device onboarding, protocol updates, user behavior variation, or adaptive attacks. This paper presents a hybrid IDS framework that integrates supervised Random Forest classification, unsupervised Isolation Forest anomaly monitoring, and Kolmogorov–Smirnov (KS)-based concept drift monitoring. In the experimental pipeline, Isolation Forest is trained exclusively on benign traffic to ensure that the anomaly detector models normal behavior rather than an attack-dominated training distribution. The evaluation uses a large-scale chronologically sampled subset of the CICIoT2023 dataset containing 3,890,621 records while preserving the natural class distribution of 2.35% benign traffic and 97.65% attack traffic. The chronological 80/20 train/test split is established first at the file level, followed by systematic sampling within each split to reduce the risk of leakage across the evaluation boundary. On the 746,094-record test set, the proposed hybrid IDS achieved 99.73% accuracy, 99.89% precision, 99.83% recall, 99.86% F1-score, and a false positive rate of 4.77%. The corresponding confusion matrix contains TN = 16,683, FP = 836, FN = 1205, and TP = 727,370, yielding 95.23% specificity and 97.53% balanced accuracy. Standalone Random Forest marginally outperformed the hybrid model in raw accuracy and false positive rate; therefore, the contribution of the proposed framework is centered on deployment-oriented anomaly monitoring, drift awareness, and generalization rather than absolute superiority in static classification metrics. A leave-one-attack-family-out experiment withholding MITM-ArpSpoofing from training showed that the hybrid model detected 85.26% of the unseen attack-family samples, compared with 85.18% for Random Forest alone and 7.05% for Isolation Forest alone. These findings provide initial evidence of generalization to one held-out attack family but should not be interpreted as proof of broad zero-day detection capability. The framework is therefore positioned as a competitive IDS that combines supervised detection with anomaly monitoring and concept drift awareness for deployment-oriented IoT security. Full article

Symmetry power integrity is a core issue for power system data acquisition. However, industrial load data integrity is affected by missing values, abnormal disturbances, and low-reliability observations. A reliability-aware cross-scene industrial load symmetry power data reconstruction method is proposed based on adversarial learning. Firstly, an industrial electricity scene classification is proposed. Temporal and frequency-domain features are jointly encoded by a multilayer perceptron. The scene affiliation of the data is identified by cosine similarity to improve the cross-scene generalization capability. Secondly, a diffusion denoising generative adversarial reconstruction framework is proposed. For missing data, a conditional diffusion model is constructed with historical temporal distributions. Data structures are recovered by forward diffusion and reverse denoising processes. For low-reliability observations, original observations, first-order differences, and second-order differences are adopted to construct local shape constraints. In addition, the residual-correction guidance mechanism is introduced to estimate and correct observation deviations to improve the data reconstruction accuracy. Finally, simulations are conducted with industrial load datasets in Liaoning Province. The results validated the effectiveness of the proposed method. The average accuracies of data correction and data reconstruction reach 97.21% and 97.17%, respectively. Moreover, reconstruction accuracies exceeding 90% are maintained in cross-scene conditions involving different seasons and regions. Full article

Hazard analysis and critical control point (HACCP) systems provide a preventive framework for food safety by implementing quality assurance plans, continuous monitoring, corrective actions, and risk mitigation strategies at critical control points throughout food processing, including dairy products such as ice cream. Artificial intelligence (AI) is increasingly transforming food safety management by enabling real-time monitoring, predictive analytics, and automated decision-making within food processing systems. This review critically examines the integration of AI technologies into HACCP systems for ice cream manufacturing, with an emphasis on improving hazard detection, process control, traceability, and the efficiency of corrective actions. The review evaluates the application of Internet of Things sensors, computer vision, and machine learning-based predictive monitoring systems across critical processing stages, including raw material reception, pasteurization, continuous freezing, and hardening/storage. Compared to conventional HACCP systems, AI-assisted technologies offer greater capabilities for anomaly detection, predictive maintenance, automated verification, and data-driven risk management. Nevertheless, their industrial implementation remains constrained by data quality limitations, infrastructure cost, cybersecurity risks, regulatory uncertainty, and limited model explainability. Accordingly, this review highlights key research gaps related to industrial scalability, validation under dynamic processing conditions, and the scarcity of ice cream-specific AI datasets. Finally, the review identifies future research directions and emerging opportunities for applying AI technologies in food processing and quality control systems, providing a framework for the evolution of intelligent HACCP systems in frozen dairy manufacturing. Full article

The rapid adoption of Internet of Things (IoT)-enabled applications has significantly expanded the cyberattack surface across a wide range of critical systems such as industrial IoT (IIoT), smart grids, transportation, healthcare, industrial control systems, and smart cities. False data injection attack (FDIA) has emerged as a serious security threat to these applications due to its stealthiness and adversarial nature, silently corrupting the data integrity of critical operational processes without triggering conventional detection mechanisms. Existing FDIA solutions rely on single-model architectures that are built based on classical or limited predefined attack scenarios. Such solutions often fail to achieve robust detection under adversarial and evolving attack conditions; accordingly, they lack generalisability and are insufficient to capture the broader scope of FDIAs. In this study, a hybrid detection framework is proposed that integrates a Random Forest classifier with an unsupervised anomaly detection model based on a deep autoencoder combined through a Logistic Regression metaclassifier. The proposed framework addresses the gap in single-model detectors that either rely on fixed decision boundaries that struggle with gradually evolving stealthy FDIA patterns or on anomaly detection that lacks strong discriminative power in separating subtle adversarial deviations from normal operational variability. Different types of stealthy and adversarial FDIA have been modelled and injected into the dataset samples for use in training the proposed model. The results show that the overall detection performance of the proposed architecture improved by 2.39 percentage points in terms of F1-score while maintaining a low false-positive rate of 0.49%. These findings reflect the effectiveness of feature representation learning via autoencoders and hybrid classification strategies against stealthy and adversarial FDIA patterns. Future work should include temporal modelling for further advancing robust detection against evolving adversarial threats. Full article

This study proposes a zero-shot cross-domain intrusion detection framework for industrial control systems (ICS) using a canonical feature representation and domain-adversarial learning. While prior approaches relied on labeled target data, the proposed method generalizes across heterogeneous SCADA datasets without target supervision. Our experimental results across SWaT, BATADAL, Mississippi, and HAI datasets show that our proposed approach outperforms traditional unsupervised baselines. Specifically, PCA-based detection achieves a ROC-AUC of 0.84 on SWaT and 0.63 on Mississippi, compared to near-random performance (0.50 ROC-AUC, 0 F1-score) for Isolation Forest and One-Class SVM due to threshold calibration limitations. A two category negative transfer taxonomy is introduced to explain performance degradation under domain divergence, and domain-adversarial analysis confirms domain-invariant representations with accuracy converging to chance level. These findings highlight the robustness of the proposed framework for real-world cross-domain ICS anomaly detection and its potential for deployment in environments with limited labeled data. Full article

Slab reheating furnaces are among the most energy-intensive units in hot-rolling production lines, and their operating states directly affect fuel consumption, temperature uniformity, and production cost. High energy consumption conditions often arise from coupled changes in reheating time, furnace-temperature regulation, combustion status, and production rhythm, making them difficult to identify using fixed energy thresholds or manual experience alone. This study proposes an energy-efficiency-constrained DDPM-ConvTransformer method for data-driven high-energy-consumption anomaly diagnosis in slab reheating furnaces. Continuous industrial records are converted into fixed-length production windows, and a denoising diffusion probabilistic model is used to learn the multivariate temporal distribution of normal operating conditions. A convolutional module and a Transformer encoder are embedded in the denoising network to capture local thermal-process fluctuations and long-range temporal dependencies. During training, a specific-energy auxiliary constraint guides the shared representation toward operating patterns associated with energy efficiency. During inference, window-level anomaly scores are obtained from diffusion denoising errors, and the decision threshold is determined from validation-set score quantiles. Using 7476 industrial production records for model development and evaluation, the test-set results show that the detected abnormal windows have 47.83% higher specific energy consumption and 62.17% higher total energy consumption than normal windows, with Cohen’s d values of 1.27 and 1.60, respectively. Compared with PCA, Isolation Forest, autoencoder-based models, an energy-constrained Transformer autoencoder, and diffusion-based baselines, the proposed method produces stronger post hoc energy-consumption differences, while computational cost analysis further clarifies its practical trade-off for offline diagnosis and periodic screening. Group analysis, typical-window diagnosis, and perturbation validation further support its applicability for energy-efficiency diagnosis and high-consumption operating-condition screening in slab reheating furnaces. Full article

Minimizing unplanned downtime is critical for maintaining productivity in modern manufacturing. While combining sensor networks with machine learning provides a practical way to detect mechanical failures early, conventional data-driven diagnostics often fail during highly transient stamping operations. This failure stems from severe spectral smearing and signal distortions caused by fluctuating process loads and variable operating speeds. To address these limitations, we present a field-tested fault diagnosis (FD) framework deployed in an active automotive components plant. Over a twelve-month observation period, we collected raw vibration and process data from two operational transfer presses, building a comparative dataset that captures both localized gear damage and healthy baseline dynamics. After preprocessing the data to isolate signal anomalies, we systematically evaluated the diagnostic performance of six algorithms: SVM, Random Forest, Naive Bayes, k-NN, Decision Trees, and Logistic Regression. By integrating angle-based position data from a high-resolution encoder, the developed framework successfully pinpointed specific defective gear teeth. Ultimately, the Random Forest model outperformed the others, delivering the most robust detection accuracy under real-world factory conditions. These results show that the proposed Condition Monitoring (CM) approach significantly reduces resource waste and prevents costly downtime, offering a practical and scalable asset management model for industrial applications. Full article