Search Results (537)

Wind turbines are increasingly deployed at larger scales and in harsher operating environments, leading to greater structural complexity, stronger load variability, and higher maintenance demands across both drivetrain and structural components. Reported field data indicate that gearboxes and bearings account for approximately 30–40% of total turbine downtime, while blade-related failures contribute roughly 20–25% of reported failure events, primarily through fatigue, delamination, leading-edge erosion, and lightning-induced defects. In parallel, large-scale and offshore turbines show increasing susceptibility to tower fatigue cracking, corrosion-assisted degradation, and flange joint bolt-preload loss under cyclic and environmental loading. This review provides a comprehensive applied-engineering synthesis of failure mechanisms, reliability challenges, and monitoring strategies for major wind turbine components, including gearboxes, bearings, blades, towers, and flange joints. A wide range of condition monitoring, structural health monitoring (SHM), and prognostics and health management (PHM) approaches is critically examined, including vibration analysis, acoustic emission, ultrasonic inspection, infrared thermography, impedance-based sensing, electromagnetic methods, machine vision, SCADA-based diagnostics, and artificial-intelligence-assisted fault classification. The review compares these techniques in terms of detectable damage types, spatial coverage, sensitivity, deployment practicality, and limitations under real operating conditions. In addition, statistical reliability methods and data-driven models are discussed to interpret failure trends and uncertainty. Recent AI-based studies have reported fault classification accuracies exceeding 90% under controlled or semi-controlled conditions; however, their field reliability remains constrained by data imbalance, domain shift, limited labeled failure datasets, model interpretability, and insufficient validation under realistic turbine operating environments. The main contribution of this review is an integrated applied synthesis that connects drivetrain and structural failure mechanisms with measurable monitoring indicators, diagnostic technologies, AI-enabled PHM limitations, and predictive-maintenance decision needs. The paper provides practical guidance for monitoring design, early fault detection, predictive maintenance, and long-term reliability improvement in next-generation wind turbine systems. Full article

Heart failure (HF) continues to be a major global health burden, with persistent morbidity and mortality despite guideline-directed and device-based therapies. Evidence suggests the gut–heart axis is a critical and underrecognized contributor to HF progression. Alterations in cardiac output and systemic venous congestion in HF lead to intestinal hypoperfusion, mucosal edema, and loss of barrier integrity, increasing intestinal permeability, gut dysbiosis, and translocation of microbial products. This systemic translocation is associated with chronic low-grade inflammation that activates innate immune pathways that correlate with endothelial dysfunction, oxidative stress, fibroblast activation, and adverse cardiac remodeling. Gut-derived metabolites derived by microbial metabolism modulate cardiovascular health by altering the metabolic profiles. Dysbiosis results in loss of protective short-chain fatty acid (SCFA)-producing bacteria and enriches pro-inflammatory taxa such as trimethylamine N-oxide (TMAO)-producing bacteria. Elevated TMAO is associated with increased mortality and hospitalization in HF, whereas SCFAs enhance barrier integrity and immune tolerance. Secondary bile acids and uremic toxins such as indoxyl sulfate and p-cresyl sulfate further link dysbiosis to fibrosis and vascular stiffness. Circulating markers such as TMAO, lipopolysaccharide-binding protein (LBP), and soluble CD14 carry prognostic value beyond traditional cardiac biomarkers. This review highlights current experimental, translational, and clinical evidence describing gut dysbiosis and its molecular links to HF progression. Targeting the gut–heart axis represents a novel therapeutic approach in HF. Dietary modulation, probiotics/prebiotics, fecal microbiota transplantation, and inhibitors of microbial metabolic pathways show promise. Future research should emphasize microbiota-based interventions in HF management. Full article

Heterogeneous fault information and a lack of real-time synchronization in CNC machine tools hinder effective Prognostics and Health Management (PHM). This paper designs and implements a digital twin-driven PHM framework for machine tools that integrates a unified machine-tool fault information dictionary and a mechanism-data dual-driven diagnostic model (ResNet-TCN). A cyber–physical platform was developed using OPC UA and RESTful APIs to ensure real-time data synchronization. Experiments on the PHM 2010 dataset demonstrate that the proposed ResNet-TCN model achieves a root mean square error (RMSE) of 5.46 μm for tool wear prediction. Its performance surpasses that of traditional LSTM models, and the proposed framework effectively eliminates information silos, providing a responsive, scalable and accurate PHM solution for smart manufacturing. Full article

Aircraft digital twins are increasingly used for diagnostics, prognostics, and predictive maintenance, but their role as lifecycle-oriented, multi-stakeholder decision-support ecosystems remains insufficiently developed. This paper addresses this gap by proposing a conceptual systems-engineering framework for an aircraft digital twin ecosystem supporting sustainable aviation transport management. The framework integrates physics-based, data-driven, hybrid, probabilistic, and federated modelling approaches and includes a three-layer ecosystem model, formal mathematical representation of aircraft and digital twin lifecycle evolution, federated model updating, lifecycle decision-support scenarios, reference architecture, validation and trustworthiness principles, and a five-level maturity model. Representative aviation industrial cases are used to interpret the framework. The analysis shows that current industrial practice already contains elements of predictive maintenance, fleet analytics, engine health monitoring, and cloud-enabled MRO optimization, but full aircraft-level lifecycle governance, sustainability trade-off analysis, federated validation, and multi-stakeholder decision orchestration remain underdeveloped. The proposed framework positions aircraft digital twins as asset-level instruments for lifecycle planning, coordinated governance, and sustainability-oriented decision support. Full article

Prognostic health management of rolling element bearings requires feature representations that reliably track degradation while remaining tractable for real-time deployment. This paper investigates whether uniform time-delay embedding can serve as a near-optimal substitute for computationally expensive non-uniform embedding in recurrence-based vibration analysis. We show empirically that optimally chosen uniform delay vectors yield phase-space reconstructions of bearing vibration signals not significantly inferior to those produced by globally optimized non-uniform delay vectors, compressing the parameter search from a combinatorial optimization to a single scalar selection. Building on this near-optimality result, we construct color recurrence plots from uniformly embedded phase spaces and apply them to remaining useful life (RUL) prediction on the Intelligent Maintenance Systems (IMS) bearing dataset. We further demonstrate that standard binary recurrence plots are poorly suited for RUL estimation: their dense and erratically varying local patterns obscure the degradation trends required for reliable prognostics. Color recurrence plots, by contrast, suppress these local instabilities by averaging recurrence structures across multiple phase-space projections, exposing a globally evolving intensity that tracks bearing health throughout its degradation trajectory. This work establishes uniform delay embedding combined with color recurrence representation as an efficient, principled, and practically deployable approach to recurrence-based condition monitoring in industrial predictive maintenance. Full article

Accurate remaining useful life (RUL) prediction of rolling bearings was essential for condition-based maintenance because bearing service degradation was primarily governed by progressive rolling-contact fatigue at the rollingelement–raceway interface, whereas vibration signals provided measurable responses to this degradation rather than being its physical cause. However, reliable RUL prediction remained challenging because vibration measurements were noisy, nonlinear, stage-dependent, and sensitive to operating-condition shifts. In this study, a health-indicator-guided temporal-attention framework was developed for bearing RUL prediction using public run-to-failure vibration datasets. The novelty of this work lay in integrating degradation-consistent health indicator construction, sliding-window life-cycle representation, and HI-guided temporal attention into a unified and interpretable prediction framework. First, degradation-sensitive vibration features were extracted and fused into a compact health indicator (HI) to represent the progressive deterioration trend. Then, sliding-window sequences were generated and processed by a Transformer-based temporal-attention network, through which long-range temporal dependencies were captured and higher weights were assigned to informative degradation segments near stage transitions and late-life acceleration. Experiments on the XJTU-SY and IMS datasets showed that the proposed method improved prediction stability, reduced late-life error amplification, and achieved better performance than baseline variants without HI or temporal attention. Ablation analysis confirmed that HI construction mitigated cross-stage drift, whereas temporal attention enhanced transition sensitivity during accelerated degradation. Robustness and cross-domain tests further indicated that the method maintained acceptable degradation-following behavior under noise perturbations and operating-condition changes, although explicit domain-adaptation mechanisms were still required for strongly shifted target domains. Full article

Accurate Remaining Useful Life (RUL) prediction of TFT-LCD modules is critical for industrial predictive maintenance, yet it remains heavily challenged by complex degradation mechanisms in different climates. Traditional purely data-driven models (SVR, LSTM) often lack physical interpretability, struggling to filter out environmental noise or predict irreversible failures. To address this, we propose a highly reliable prognostic tool based on a Physics-Informed Gaussian Process Regression (PI-GPR) framework, by embedding cumulative thermal load and thermo-mechanical stress into the model’s prior function. Evaluated using one-year field exposure data, the physical constraints empower the model to accurately predict device lifetime under highly variable environments, including luminance fluctuations in tropical hygrothermal conditions and device failures in cold environments. Quantitative results demonstrate that the unified PI-GPR framework achieves an outstanding coefficient of determination (R² = 0.93) and reduces the RUL prediction error to merely 7.5 days, significantly outperforming conventional shallow learning, deep sequence, and standard probabilistic baselines. Ultimately, this study provides a robust, physically grounded methodology for the health monitoring and life cycle management of display modules in practical industrial applications. Full article

Multimodal sensor fusion is increasingly used to improve observability for fault diagnosis and prognostics, enabling Remaining Useful Life estimation in complex mechatronic and robotic systems. Yet, real-world deployments remain vulnerable to sensor faults and data integrity issues—including bias and drift, miscalibration, dropouts, saturation, cross-talk, time desynchronization, and domain shift—which can propagate through fusion pipelines and lead to optimistic validation and poor generalization. These challenges are particularly consequential in safety- and health-adjacent applications such as collaborative robots, wearable/rehabilitation devices, and human-centric mechatronic systems where decisions based on faulty sensing may affect both reliability and user safety. This review synthesizes the state of the art on (i) sensor fault taxonomies and fault models relevant to multimodal fusion, (ii) fault-aware fusion strategies spanning data-, feature-, and decision-level integration, and (iii) how sensor faults and uncertainty impact diagnosis and remaining-life estimators. We will conduct a systematic scoping review of peer-reviewed literature, extracting sensor modalities, fault characterization or injection protocols, fusion architectures, validation settings (simulation, hardware-in-the-loop, bench, and in-field/on-body studies), and reporting completeness. Beyond summarizing methods, we provide practical reporting standards for sensor-fusion-based diagnosis and prognostics, including a minimum disclosure set covering synchronization, fault ground truth, missingness handling, leakage controls, uncertainty calibration, and task-relevant metrics. Reusable checklists and evidence tables are included to support more comparable, reproducible, and deployment-ready research. Full article

Structural health monitoring (SHM) of fibre-reinforced composites requires a health indicator that is monotonically non-decreasing under the standard SHM assumption that no self-healing or maintenance-induced restoration event is active, derived from heterogeneous sliding-window observations of acoustic emission, strain, and fibre Bragg grating channels, with only the failure timestamp available per specimen. Conventional endpoint-supervised regressors attain high rank correlation with normalised life but produce jagged, non-monotone trajectories of limited engineering value. A method named SAMS-Net (Smoothness-Anchored Monotone Neural Differential Equation Network) is developed, in which a neural differential equation backbone is anchored by a two-level Pool-Adjacent-Violators (PAV) projection. A within-window projection is applied during training with a straight-through gradient, and an across-window projection is applied at inference, yielding a globally non-decreasing health indicator. A smoothness-stratified two-phase training schedule first trains on specimens whose per-specimen median local-smoothness coefficient exceeds 0.5, then fine-tunes on the full set. Across the present seventeen-specimen open-hole carbon-fibre dataset spanning two stress levels and six leave-one-specimen-out and cross-condition scenarios, SAMS-Net wins on every scenario on the canonical Prognostics and Health Management (PHM) Composite of monotonicity, trendability, and robustness, with margins of 0.22 to 0.48 against the strongest baseline, reproducible across three random seeds. Ablation reveals that the operative mechanism is the two-level PAV projection rather than the stochastic differential equation (SDE) inductive bias. A new control experiment in which the across-window PAV projection is applied at inference to the strongest baselines confirms that the projection accounts for a substantial share of the SAMS-Net margin, while the within-window training-time projection and a globally consistent prognosability metric retain a SAMS-Net advantage. Cross-site or cross-material transferability remains to be established in future work. Full article

Background: Healthcare-associated infections (HAIs) and antimicrobial resistance are major global public health challenges, influenced by patient clinical complexity and prescribing practices. Methods: Three-point prevalence surveys (PPSs) were conducted (P1: November 2024; P2: June 2025; P3: November 2025), involving 456 patients at the University Hospital “San Giovanni di Dio e Ruggi d’Aragona”, Salerno, Italy. Results: The prevalence of HAIs fluctuated between 3.1% (P1) and a peak of 6.1% (P2), before decreasing to 1.9% (P3), correlating with the presence of multidrug-resistant pathogens in critical care areas. The prevalence of antibiotic use remained stable (~48%), with a decrease in carbapenem use (from 12% to 9%). A decline in ‘unknown’ McCabe scores from 24.6% to 6.8% (p < 0.001) was also observed, suggesting an improvement in completeness of prognostic data, although changes in data collection practices may also have contributed to this change. Conclusions: We showed an association between clinical severity, prolonged hospitalization, invasive device use, and infection risk in a single tertiary-care hospital, within an exploratory, cross-sectional framework. Despite high healthcare pressure, improvements were observed in antimicrobial stewardship and clinical surveillance. Future strategies should focus on optimal device management and on extending surveillance activities to medical wards with increasing patient complexity. Full article

Background and Objectives: Malnutrition is common among older adults with fragility fractures and is linked to poor clinical outcomes in orthopedic surgery. However, the association between nutritional status and bone-related parameters, including bone mineral density (BMD) and bone turnover markers, remains inadequately characterized in this population. This study evaluated these associations in patients with proximal femoral fragility fractures. Materials and Methods: In total, 108 patients who underwent surgery for proximal femoral fragility fractures were retrospectively analyzed. Nutritional status was evaluated using the Geriatric Nutritional Risk Index (GNRI), Prognostic Nutritional Index (PNI), and Controlling Nutritional Status (CONUT) score. BMD was measured at the femoral neck of the proximal femur and the lumbar spine using dual-energy X-ray absorptiometry. Serum 25-hydroxyvitamin D (25[OH]D) and bone turnover markers, including total procollagen type I N-terminal propeptide and tartrate-resistant acid phosphatase 5b, were also evaluated. Correlation analyses, group comparisons, and multivariate linear regression analysis were performed to identify factors independently associated with femoral neck BMD. Results: GNRI and PNI were significantly positively correlated with femoral neck BMD (r = 0.48 and r = 0.26, respectively; both p < 0.01), while the CONUT score showed a significant negative correlation (r = −0.27, p < 0.01). Nutritional indices were not significantly correlated with lumbar spine BMD. Patients classified as malnourished by GNRI or PNI had significantly lower femoral neck BMD and serum 25(OH)D and higher bone turnover markers than well-nourished patients. Multivariate linear regression analysis revealed that GNRI, PNI, and CONUT score remained independently associated with femoral neck BMD after adjusting for age, sex, and body mass index. Conclusions: Nutritional status assessed by hematological indices was significantly associated with femoral neck BMD and bone metabolism markers in patients with proximal femoral fragility fractures. Findings underscore the importance of nutritional status in bone health and should be considered in the management of osteoporosis and fragility fractures. Full article

Central nervous system (CNS) tumors represent a heterogeneous group of neoplasms associated with significant morbidity and mortality despite their relatively low incidence. Advances in the fifth edition of the World Health Organization (WHO) classification have emphasized the integration of histopathological, immunohistochemical, and molecular features, fundamentally transforming diagnostic and prognostic frameworks in neuro-oncology. This manuscript aims to provide an overview of CNS tumor biology, focusing on key diagnostic markers, genetic and epigenetic alterations, and emerging therapeutic strategies. It further describes recent advances in multi-omics approaches and artificial intelligence, which enable deeper characterization of tumor heterogeneity and support the development of precision medicine strategies. Finally, current and emerging therapeutic modalities, including combination therapies, targeted treatments, and novel molecular targets, are examined with emphasis on overcoming resistance mechanisms and improving clinical outcomes. Overall, the integration of molecular biology, advanced diagnostics, and innovative therapeutic approaches represents a critical step toward personalized management of CNS tumors and improved patient survival. Full article

Background/Objectives: Complex chronic patients represent a heterogeneous and high-risk population, for whom accurate prognostic tools are essential to guide clinical decision-making, optimize resource allocation, and support tailored interventions. The PROFUND index was developed for mortality prediction in polypathological patients, but its performance has not yet been evaluated in an ambulatory integrated care model. Methods: A retrospective observational study was conducted using two cohorts. Cohort H included complex chronic patients admitted to the Internal Medicine Department between March 2023 and February 2024. Cohort A comprised complex chronic patients followed by a multidisciplinary chronic care program between November 2016 and December 2023. PROFUND scores were derived from electronic health records. Discrimination for 12-month mortality was assessed using Kaplan–Meier curves, log-rank tests, and receiver operating characteristic curve analysis. Calibration was evaluated by comparing observed mortality with expected mortality based on the original PROFUND index and improved through intercept and slope recalibration. Results: A total of 660 patients were included in cohort H and 540 in cohort A. One-year mortality was 38.0% and 30.2%, respectively. Discriminatory performance was good in hospitalized patients (AUC 0.760; 95% CI 0.724–0.797) and moderate to good in ambulatory patients (AUC 0.705; 95% CI 0.656–0.754). Calibration analyses demonstrated systematic overestimation of mortality, particularly in the ambulatory cohort and intermediate–high risk strata, while recalibration improved agreement between predicted and observed risks. Conclusions: The PROFUND index provides useful risk stratification for 12-month mortality in CCP across care settings but overestimates absolute risk, particularly in ambulatory case management populations. Local recalibration may improve prognostic accuracy, support individualized care planning, and advance care planning discussions and allocation of multidisciplinary follow-up intensity. Full article

Individuals with cystic fibrosis (CF) face significant treatment burdens, and as life expectancy has increased, there is growing emphasis on their psychosocial well-being. Prevalence data indicate that approximately one-quarter to one-third of individuals with CF and their caregivers experience clinically significant anxiety or depression. Specifically, pooled global estimates report an anxiety prevalence of 24.9% (95% CI: 20.8–28.9%) and depression prevalence of 13–33% in adults with CF, with caregivers experiencing even higher rates (anxiety: 35–38%; depression: 20–35%). Depression is independently associated with a nearly twofold increase in mortality risk and substantially higher healthcare costs, underscoring its prognostic significance. These mental health comorbidities are consistently associated with reduced treatment adherence, diminished quality of life, increased healthcare utilisation, and decreased survival. Accordingly, psychological well-being has emerged as a key patient outcome that directly shapes engagement with care and the effectiveness of long-term CF management. International CF guidelines now recommend routine mental health screening within multidisciplinary care frameworks. Evidence-based interventions include cognitive–behavioural therapy (CBT), which is endorsed as a primary treatment, although access remains limited, and stepped-care pharmacotherapy, primarily selective serotonin reuptake inhibitors (SSRIs), for moderate to severe symptoms. Telemedicine and other digital health approaches have expanded access to psychological support, with remote CBT and online programmes demonstrating feasibility and symptom improvement during the COVID-19 pandemic and beyond. The advent of CFTR modulator therapies has significantly altered clinical outcomes, enabling many patients to achieve improved lung function and daily functioning. Nevertheless, mental health challenges persist, as individuals navigate new identity shifts and anxieties despite enhanced physical health. The implementation of mental healthcare remains inconsistent; while screening rates have increased, timely follow-up and integrated psychosocial support are frequently insufficient across care centres. This narrative review highlights the ongoing need to integrate mental health management into CF care to optimise adherence, patient outcomes, and long-term survival in the current therapeutic landscape. Full article

This study investigates the integration of categorical inverter alarm data into data-driven frameworks for photovoltaic (PV) system monitoring. While most existing approaches rely exclusively on continuous SCADA measurements, the potential of categorical operational data remains largely unexplored. In this work, categorical alarm signals are incorporated into power forecasting to enable anomaly detection. The proposed approach is evaluated on a large-scale real-world dataset comprising multiple PV plants and more than 100 inverters, representing over 1000 inverter-years of operation. The four most popular time series forecasting models, including Multi-Layer Perceptron, Long Short-Term Memory, Extreme Gradient Boosting, and Mamba, are used to estimate power output from continuous inputs, while categorical variables are integrated using one-hot encoding and entity embeddings. Anomaly detection is performed by analyzing residuals between predicted and measured power output. The results show that categorical alarm data contain relevant operational information and can be effectively incorporated into forecasting-based monitoring frameworks. However, their impact on predictive performance varies depending on the encoding strategy and model choice, highlighting important trade-offs between model complexity and feature representation. By providing a systematic evaluation of categorical data integration across a large, diverse dataset, this work addresses a gap in the literature and establishes a benchmark for future research on hybrid continuous–categorical approaches for PV inverter monitoring. Full article