Search Results (174)

Intelligent mechanical fault diagnosis plays a key role in maintaining rotating machinery. Although data-driven unsupervised domain adaptation methods have achieved considerable progress, their industrial applications are often restricted by low-quality sensor data. Non-stationary vibration signals and background noise easily corrupt target pseudo-labels, while conventional methods focusing on global statistical matching usually neglect local structures, leading to confirmation bias under dynamic loads. To improve diagnostic reliability, we propose a Noise-Resilient Whitened Domain Adaptation (NRWDA) framework. To handle covariance fluctuations caused by changing working conditions, a Lipschitz-bounded Temporal Whitening (LTW) module is designed as a low-pass filter. An Entropy-guided Prototype Truncation (EPT) mechanism is adopted to discard ambiguous labels and better calibrate semantic centers. In addition, a Dispersion-Adaptive Contrastive Sharpening (DACS) strategy is introduced to dynamically adjust the contrastive temperature based on predictive dispersion, thus tightening decision boundaries. The proposed method is evaluated on CWRU, PU, and MFPT datasets. The PU dataset, featuring fluctuating loads and non-stationary signals, poses a strict test, yet our model maintains its stability even at a 0 dB SNR—a condition where standard approaches usually break down. During the $P0\to P3$ transfer task involving substantial radial force variations, NRWDA secures a 72.36% accuracy and surpasses established baselines. These findings confirm that our technique successfully isolates dependable diagnostic features from corrupted sensor measurements within actual industrial settings. Full article

The large-scale integration of variable renewable energy sources introduces critical challenges of intermittency and uncertainty, yet consumption forecasting, generation forecasting, and anomaly detection are typically addressed in isolation, neglecting the bidirectional feedback between consumption patterns, generation mix, and public decision-making. This PRISMA 2020-compliant systematic review compared statistical, machine learning, and deep learning models for energy forecasting and machine learning and deep learning models for anomaly detection. Searches in Google Scholar and Scopus used seven targeted strings, restricted to peer-reviewed empirical studies (2022–2026; 2023–2026 for anomaly detection), indexed in Q1–Q3 JCR journals, excluding theoretical and non-benchmarked works. A six-item risk of bias questionnaire—with a threshold of four points—guided inclusion, yielding 60 articles. Addressing the first research question (RQ1) on comparative model performance, hybrid deep learning architectures optimized with bio-inspired metaheuristics achieved the highest forecasting accuracy ($R^2$ up to 0.9984), with metaheuristic optimization acting as a cost-reducing factor; statistical models remained competitive for long-horizon forecasting, while large-language-model-based approaches addressed data scarcity through few-shot learning. Addressing the second research question (RQ2) on smart grid optimization, predictive techniques reduce forecasting errors enabling real-time load adjustment and Demand Response, though a systematic asymmetry constrains their potential: consumption studies integrate socio-economic variables, whereas generation studies rely on meteorological inputs. Addressing the third research question (RQ3) on infrastructure security, supervised and unsupervised approaches detect anomalous operational states and support fault diagnosis, yet remain constrained by scarce labeled fault data and limited cross-regional validation; generative models such as GANs and diffusion models partially address this limitation by enabling Sim2Real strategies and realistic digital twin construction. Evidence is strongest for hybrid forecasting; certainty is lower for anomaly detection given reliance on experimental surrogates. No single paradigm achieves universal superiority. The primary finding is the consistent absence of integrated frameworks jointly modeling consumption, generation, anomaly detection, and public decision-making across the reviewed literature. This result reflects a structural limitation of the current state of the art, rather than a forward-looking research agenda. This study was funded by the ENIA International Chair on Trustworthy Artificial Intelligence European Recovery Plan; the protocol was not pre-registered. Full article

Intelligent detection in modern manufacturing, healthcare, process industries, and structural monitoring is fundamentally enabled by heterogeneous sensor systems. Rather than being viewed as a purely image-centered recognition task, intelligent detection is more appropriately formulated as a sensor-driven state inference problem in which sensing physics, signal quality, temporal synchronization, modality availability, and deployment conditions jointly determine what can be reliably detected, localized, interpreted, and acted upon. Against this background, this review provides a structured synthesis of the field through three coupled dimensions, namely methods, systems, and governance, and organizes the literature around four recurring engineering components: signal unification, representation unification, alignment mechanisms, and robustness mechanisms. Using a structured review protocol with explicit source selection, screening, and study coding, the paper traces the methodological evolution from traditional feature-engineering and model-based pipelines to deep learning for visual, temporal, multimodal, generative, and mechanism-constrained sensing, and further to foundation-model-based and multimodal sensor intelligence. Cross-domain evidence is synthesized from industrial defect detection, fault diagnosis, remaining useful life prediction, non-destructive testing, structural health monitoring, medical lesion analysis, and process monitoring. The review argues that recent progress has substantially strengthened learned representations, multimodal interaction, and semantic extensibility, but has not removed persistent constraints arising from domain shift, missing modalities, calibration instability, privacy-preserving collaboration, and edge-side resource limits. Accordingly, the central challenge is no longer how to optimize isolated detection models, but how to build sensor-enabled intelligent systems that remain physically grounded, trustworthy, transferable, and maintainable under real operational conditions. On this basis, the paper concludes by identifying future directions in mechanism-aware modeling, trustworthy evaluation, missing-modality-robust multimodal systems, privacy-preserving cross-site collaboration, and edge-native lifecycle-aware deployment. Full article

Digital transformation in higher education increasingly hinges on the robustness and governability of Information and Communication Technology (ICT) infrastructures, with campus Wi-Fi networks serving as the operational backbone of digital learning, research collaboration, and administrative services. In large universities, these networks typically evolve into heterogeneous, multi-vendor environments, introducing ongoing challenges in monitoring coherence, configuration governance, and cross-platform performance diagnosis. Despite the centrality of these issues, smart campus scholarship has paid limited attention to day-to-day operational management. This study examines the design and operational performance of a dual-platform Wi-Fi network management architecture implemented at Mahasarakham University, Thailand. The architecture strategically integrates SolarWinds and LibreNMS to combine centralized network-wide visibility with fine-grained, device-level diagnostics across a multi-vendor infrastructure. An engineering-oriented mixed-method approach was employed, drawing on production monitoring logs and semi-structured interviews with campus network engineers. Findings indicate that SolarWinds strengthens configuration oversight and campus-level situational awareness, whereas LibreNMS enhances detailed performance analytics and accelerates fault isolation. Their coordinated deployment improves operational stability, diagnostic clarity, and long-term maintainability of campus Wi-Fi systems. The study provides practical architectural guidance for managing heterogeneous ICT infrastructures in smart campus and enterprise-scale environments. Full article

Process monitoring methods play a crucial role in identifying equipment malfunctions and instrument failures, as well as in maintaining process safety and product quality. Selecting the right approach for fault detection and diagnosis is therefore vital. Several localization methods based on Kernel Principal Component Analysis (KPCA) exist, such as the partial localization approach, which is effective at detecting anomalies but does not always pinpoint faults precisely. This method often identifies a suspicious area or group of variables without isolating the exact source of the fault. In complex systems such as chemical reactors, it can produce false positives or incorrect localizations if the data are noisy or if the fault affects multiple correlated variables. Conversely, the reconstruction-based contribution approach, when integrated with Kernel Principal Component Analysis (KPCA), is both widely documented in the literature and highly effective for fault localization. This method first identifies anomalies using the Hotelling’s T² statistic and Q (squared prediction error) statistic, then analyzes the contributions of individual variables to these indices in order to isolate the fault. However, the convergence of the optimization algorithm using the T² index is not guaranteed. To address this limitation, we introduce RBC-KGLRT, a novel localization framework that integrates reconstruction-based contribution with KPCA and the Generalized Likelihood Ratio Test in its kernel form to improve both precision and reliability in localization tasks. This work transforms traditional KPCA and reduced-rank KPCA fault detection approaches—enhanced by the KGLRT metric—into a powerful fault localization solution through the reconstruction-based contribution (RBC) method. Its effectiveness is rigorously evaluated using the Tennessee Eastman Process (TEP), a widely recognized simulation benchmark in process control and chemical engineering. Full article

Unbalanced supply voltage (USV) represents a critical power quality challenge in industrial environments, significantly degrading the performance, efficiency, and operational lifespan of three-phase induction motors. Accurate real-time estimation of sequence impedances ($Z_a,Z_b,Z_c$) and detection of the Negative Voltage Factor (NVF) are essential for effective condition monitoring and preventive maintenance strategies. While existing machine learning methods have demonstrated promising accuracy, they often rely on manual feature engineering, lack hierarchical representation learning, and treat impedance estimation and fault detection as isolated tasks. This paper proposes a unified Deep Multi-Task Learning framework that leverages Residual Multilayer Perceptron (ResMLP) architectures for feature-based learning and Temporal Convolutional Networks (TCNs) for end-to-end raw signal learning. Our contributions include: (1) introduction of a Multi-Head ResMLP architecture that jointly optimizes phase impedance and fault detection, achieving superior NVF accuracy (MAE = 0.0007) and a fault detection F1-score of 0.8831; (2) investigation of raw-voltage TCN models for voltage-only diagnostics, with analysis of the trade-offs between end-to-end learning and feature-based approaches; (3) extensive ablation studies demonstrating the impact of network depth, data augmentation, and training protocols on model generalization; and (4) deployment of PyTorch (v2.0.1)-based models suitable for embedded systems with real-time inference capabilities (2.3 ms per prediction). Experimental validation on a 1.1 kW three-phase motor dataset under diverse load conditions (0–10 Nm) and USV magnitudes (5–15 V) confirms the robustness and practical applicability of the proposed approach for industrial fault diagnosis and condition monitoring systems. Full article

In traditional wind turbine (WT) operation and maintenance, fault diagnosis and repair have long been relied on, yet the demand for continuous operation under faults persists. To address this, this study proposes a triggered fault-tolerant control framework for wind turbines with zonotope-based interval estimation. The method enhances safety from point to range estimation of FDI, reduces network traffic load via a WT load region-based adaptive event-triggered mechanism, and enables fast, robust fault diagnosis/isolation using interval residuals. A damage equivalent load (DEL)-sensitive cost term balances structural fatigue suppression while ensuring power tracking and safety constraints. Theoretically, Linear Matrix Inequality (LMI) conditions based on common quadratic Lyapunov ensure closed-loop stability and bounded observation errors, with proven interval residual fault sensitivity and triggering reliability. Numerically, on the standard NREL 5-MW WT model under multi-conditions (turbulence, faulty communication), it achieves an average power tracking accuracy of 95.56%, 28.68% fatigue suppression, and 67.40% bandwidth saving. Overall, it synergistically optimizes robust estimation, economical communication, and fatigue-aware control, providing a theoretically rigorous and experimentally validated technical framework for engineering-scale WT reliability improvement and lifespan extension. Full article

This paper highlights a communal fault detection and isolation framework integrating a convolutional neural network (CNN) with a finite-state machine (FSM). The proposed concepts ensure state-based controlled discriminate pattern recognition and enable the diagnosis of different anomalies in the mobile robot in a multi-robot environment. The framework processes the time-series sensor data through the convolution layer upon experiencing different types of fault and governs different states based on fault diagnosis and recovery. The proposed concept has been validated using a Python 3.11 and Webot environment featuring the shrimp robot in a multi-robot arena. The model obtained an accuracy of 97% in identifying and classifying faults, enabling automated recovery of faulty robots in the multi-robot environment. Experiments conducted on different simulators demonstrate that effective fault management can be achieved with low training loss. Full article

Rolling-element bearing faults are a primary mechanical failure mode in rotating systems. In Permanent Magnetic DC (PMDC) motor applications operating under variable torque, vibration-based diagnosis is affected by load-dependent excitation and commutation-induced disturbances, which introduce amplitude bias and reduce the reliability of conventional statistical features. This study proposes Cross-Reference Energy Attention (CREA), a physics-guided dual-sensor feature framework for three-class bearing states in PMDC motor systems. CREA isolates fault-relevant content within a hardware-agnostic, empirically selected mid-frequency carrier band and incorporates a spatially separated reference sensor to evaluate transmission consistency. This design suppresses disturbances generated locally by the motor while retaining structurally transmitted bearing signatures. Experiments were conducted on a PMDC motor dynamometer with seeded bearing defects under controlled torque variation. GroupKFold cross-validation was implemented using the acquisition run as the grouping variable to prevent data leakage across runs. Under per-run normalization designed to eliminate amplitude memorization, conventional motor-side baseline features degraded to 0.495 ± 0.110 window-level accuracy, whereas the four-feature CREA representation maintained 0.999 ± 0.002. Systematic ablation and SHAP analysis demonstrate that carrier-band energy features provide the dominant discriminatory contribution, while cross-sensor interaction metrics supply complementary transmission validation consistent with the underlying mechanical model. Full article

Satellite swarm operations require robust methodologies to distinguish between leader-induced reference frame biases (global errors) and individual follower anomalies (local deviations). This is the challenge of distributed fault diagnosis. In leader–follower topologies, distinguishing between a global reference error (leader satellite broadcasting incorrect navigation data) and a local node error (follower satellite drifting) is mathematically ambiguous when we use standard methods. Even recent unsupervised frameworks, such as Model-Guided Online Transfer Learning (MGOTL), that excel at single-satellite component diagnosis, suffer from adaptation and signal bleed when they are applied directly to distributed topologies. Therefore, we propose the Isolation-First Consensus Anomaly Detection (IF-CAD) framework for Decoupling Global and Local Faults in Satellite Swarms. We introduce a Smart Freeze mechanism to prevent the learning of persistent faults and a hierarchical logic that prioritizes local isolation over global agreement. The IF-CAD framework successfully decouples global leader faults from local follower faults. Fault detection remains stable even during long-duration anomalies. Full article

Reliable fault diagnosis of induction motors from current signals is critical for preventing failures in industrial systems. However, deep learning models often exhibit performance degradation when the torque load and other operating conditions change. Although a lot of research has been completed on supervised fault classification using current signals, the investigation of the behaviour of these datasets for unsupervised learning has not been done. This study quantifies and analyses the “shadowing effect” of operational variability, demonstrating that a baseline 1D-CNN achieving 100% accuracy under static 0 Nm loads drops to 53.19% accuracy when subjected to 4 Nm load in the KAIST dataset using a stator current. Similar trends were validated using the Paderborn University (PU) bearing dataset. Using 1D-CNN feature extraction followed by Principal Component Analysis (PCA), t-SNE, and hierarchical clustering, we show that standard linear mitigation strategies, such as removing high-variance principal components, are ineffective because fault and load features are deeply entangled. Hierarchical clustering analysis confirms that the feature space is organised by load dominance, with the primary tree split consistently occurring by torque load rather than fault type. Crucially, we identify that internal geometric metrics, such as “spread” and “diameter”, correlate with external purity metrics like the proposed “Dominance Score”. The findings establish a quantitative basis for developing unsupervised, load-invariant diagnostic models that utilise geometric stopping criteria to isolate fault clusters without using ground-truth labels. Full article

High-strength bolted connections are fundamental load-bearing components in critical engineering infrastructures such as wind turbines, bridges, and heavy machinery. Under complex service environments involving dynamic loading, vibration, corrosion, and temperature variations, bolts are prone to interacting failure mechanisms, including fatigue fracture, corrosion-assisted cracking, hydrogen embrittlement, and progressive preload loss, which pose significant challenges for reliable condition monitoring and early fault diagnosis. This review provides a structured synthesis of recent advances in bolt health monitoring and intelligent fault diagnosis. A unified framework is established to link multi-physics failure mechanisms with multi-modal sensing technologies and data-driven diagnostic methods. Key sensing approaches—such as piezoelectric impedance techniques, ultrasonic phased array inspection, and computer vision-based monitoring—are critically reviewed in terms of their physical principles, diagnostic capabilities, and limitations. Furthermore, the transition from traditional model-based and signal-processing-driven methods to machine learning- and deep learning-based approaches is examined, with emphasis on multi-modal data fusion, real-time monitoring, and lifecycle-oriented health management enabled by IoT and digital twin technologies. Finally, key challenges and future research directions toward robust and scalable intelligent bolt health management systems are outlined. This review’s primary contribution lies in establishing a novel, integrated framework that links failure physics to sensing and diagnosis, thereby providing a structured roadmap for transitioning from isolated component monitoring to lifecycle-oriented, intelligent health management systems for critical bolted connections. Full article

The reliability of the Medium-Voltage Direct-Current (MVDC) power supply system is crucial for train operation, as it powers control, communication, and other critical onboard systems. Accurately locating insulation faults within this system can significantly reduce troubleshooting difficulty and prevent major operational losses. This study addresses a key challenge in applying Time-Domain Reflectometry (TDR) for fault location in single-core cables of IT systems: the incident-end impedance mismatch caused by the variable characteristic impedance of such cables, which fluctuates with installation distance from a ground plane. First, the mechanism through which this mismatch attenuates the primary fault reflection and generates secondary reflections is theoretically modeled. A resistive-capacitive (RC) coupling network is then designed to achieve bidirectional impedance matching between the test equipment and the cable under test while maintaining essential DC isolation. Simulation and experimental results demonstrate that the proposed network effectively mitigates the mismatch issue. In experiments, it increased the proportion of the primary reflected wave entering the receiver by over 30 percentage points and suppressed the secondary reflection by approximately 80%. These improvements enhance waveform clarity and signal strength, directly leading to more accurate fault location. The proposed solution, validated in a railway context, also holds significant potential for improving insulation fault diagnosis in analogous high-voltage cable applications, such as electric vehicle powertrains. Full article

A robust methodology for parametric fault diagnosis in photovoltaic systems is proposed, focusing on DC-DC boost converters. The methodology uses Adaptive Sliding Mode Observers (ASMO) combined with adaptive thresholding. Specifically, an observer-based scheme detects and isolates faults in passive components of the converter, achieving complete isolation in about 0.05 s, even under varying environmental conditions. In addition, a dynamic fault discrimination approach is introduced, based on adaptive thresholds derived from Exponentially Weighted Moving Average (EWMA). This minimizes false alarms caused by transient conditions. Stability and robustness are guaranteed through Lyapunov-based conditions. Simulation results under sequential and simultaneous fault scenarios confirm rapid and precise fault detection, highly specific isolation, and exceptional resilience against environmental disturbances. Full article

A three-phase inverter faces the risk of open-circuit (OC) and short-circuit (SC) faults in operation and requires real-time fault diagnosis. However, existing diagnosis methods have the following limitations: (1) insufficient rapid diagnosis capability for multi-switch cascaded faults; (2) inability to achieve diagnosis for hybrid OC and SC faults. To address these issues, this paper proposes a diagnosis method for cascaded switch open/short-circuit fault in a three-phase inverter based on a two-stage interval sliding mode observer (ISMO). First, by establishing a mixed logic dynamic (MLD) model considering open- and short-circuit faults, the different fault operating states of the three-phase inverter can be fully characterized. Furthermore, a two-stage cascaded ISMO was designed. The pre-stage ISMO rapidly detects abnormal status and fault phase, while the post-stage ISMO accurately isolates OC and SC faults. After diagnosis, the corresponding fault identification of the observer is set for the next fault diagnosis, achieving the sequential diagnosis of cascaded faults. The proposed diagnosis method was tested to validate its effectiveness. Full article