Search Results (287)

Civil aircraft that have obtained airworthiness certification—operating with complex structures under harsh service environments—are prone to abnormal states and potential failures. Aircraft health management, as a comprehensive integration of advanced technologies, embodies the overall engineering capability of civil aviation. The advent of big data has introduced new opportunities and challenges, driving the development of intelligent health management across the entire life cycle—from predictive strategies and real-time monitoring to anomaly detection and adaptive decision support. This paper reviews current applications and technological trends in big data-driven health management for all airworthiness-certified civil aviation aircraft, with a focus on real-time fault diagnosis, Remaining Useful Life (RUL) prediction, large-scale fault data analytics, and emerging approaches enabled by generative models. The analysis highlights the role, necessity, and future directions of these technologies in advancing sustainable and intelligent civil aviation. Full article

Turbocharged diesel engines are widely used for the propulsion and as the generators for powering auxiliary systems in marine applications. Many works were published on the development of diagnosis tools for the engines using data from simulation models or from experiments on a sophisticated engine test bench. However, the simulation data varies a lot with actual operational data, and the available sensor data on the actual vessel is much less compared to the data from test benches. Therefore, it is necessary to develop anomaly prediction and fault diagnosis models from limited data available from the engines. In this paper, an artificial intelligence (AI)-based anomaly detection model and machine learning (ML)-based fault diagnosis model were developed using the actual data acquired from a diesel engine of a cargo vessel. Unlike the previous works, the study uses operational, thermodynamic, and vibration data for the anomaly detection and fault diagnosis. The paper provides the overall architecture of the proposed predictive maintenance system including details on the sensorization of assets, data acquisition, edge computation, and AI model for anomaly prediction and ML algorithm for fault diagnosis. Faults with varying severity levels were induced in the subcomponents of the engine to validate the accuracy of the anomaly detection and fault diagnosis models. The unsupervised stacked autoencoder AI model predicts the engine anomalies with 87.6% accuracy. The balanced accuracy of supervised fault diagnosis model using Support Vector Machine algorithm is 99.7%. The proposed models are vital in marching towards sustainable shipping and have potential to deploy across various applications. Full article

This paper presents OpS-EWMA-LLM (Operational State Shifts Detection using Exponential Weighted Moving Average and Labeling using Large Language Model), a hybrid framework that combines fleet-normalized statistical shift detection with LLM-assisted diagnostics to identify and interpret operational state changes across heterogeneous fleets. First, we introduce a residual-based EWMA control chart methodology that uses deviations of each component’s sensor reading from its fleet-wide expected value to detect anomalies. This statistical approach yields near-zero false negatives and flags incipient faults earlier than conventional methods, without requiring component-specific tuning. Second, we implement a pipeline that integrates an LLM with retrieval-augmented generation (RAG) architecture. Through a three-phase prompting strategy, the LLM ingests time-series anomalies, domain knowledge, and contextual information to generate human-interpretable diagnostic insights. Finaly, unlike existing approaches that treat anomaly detection and diagnosis as separate steps, we assign to each detected event a criticality label based on both statistical score of the anomaly and semantic score from the LLM analysis. These labels are stored in the OpS-Vector to extend the knowledge base of cases for future retrieval. We demonstrate the framework on SCADA data from a fleet of wind turbines: OpS-EWMA successfully identifies critical temperature deviations in various components that standard alarms missed, and the LLM (augmented with relevant documents) provides rationalized explanations for each anomaly. The framework demonstrated robust performance and outperformed baseline methods in a realistic zero-tuning deployment across thousands of heterogeneous equipment units operating under diverse conditions, without component-specific calibration. By fusing lightweight statistical process control with generative AI, the proposed solution offers a scalable, interpretable tool for condition monitoring and asset management in Industry 4.0/5.0 settings. Beyond its technical contributions, the outcome of this research is aligned with the UN Sustainable Development Goals SDG 7, SDG 9, SDG 12, SDG 13. Full article

Congenital Anomalies of the Kidney and Urinary Tract (CAKUT) are among the most common congenital malformations and the leading cause of chronic kidney disease in children. They arise when key steps in kidney development are disrupted, including ureteric bud induction, branching morphogenesis and nephron progenitor differentiation. These processes depend on coordinated transcriptional programs, signaling pathways, ciliary function and proper extracellular matrix (ECM) organization. Advances in whole exome and whole genome sequencing, as well as copy number variation analysis, have expanded the spectrum of known monogenic causes. Pathogenic variants have now been identified in major transcriptional regulators and multiple ciliopathy-related genes. Evidence also points to defects in central signaling pathways and changes in ECM composition as contributors to CAKUT pathogenesis. Clinical presentations vary widely, shaped by modifying effects of genetic background, epigenetic regulation and environmental influences such as maternal diabetes and fetal hypoxia. Emerging tools, including human kidney organoids, gene-editing approaches and single-cell or spatial transcriptomics, allow detailed exploration of developmental mechanisms and validation of candidate pathways. Overall, CAKUT reflects a multifactorial condition shaped by interacting genetic, epigenetic and environmental determinants. Integrating genomic data with experimental models is essential for improving diagnosis, deepening biological insight and supporting the development of targeted therapeutic strategies. Full article

Fault detection and diagnosis (FDD) in pumps is crucial for building energy management by detecting the abnormal operation status, increasing the service life of equipment, and enhancing the energy performance of buildings. Most FDD methods predominantly rely on single-source data, such as building automation (BA) data or vibration data. However, sensors in BA systems are prone to inaccuracies, which consequently impedes the performance of FDD algorithms. This paper proposes a novel FDD method for pumps based on multi-source data, which integrates traditional BA electrical power data with non-intrusive measurements, including audio data, vibration data, and infrared thermal images. The method includes two stages: (1) multi-source data anomaly detection and (2) pump fault diagnosis. Various fault scenarios were tested on an experimental platform. The results demonstrate that the proposed method can effectively diagnosis pump faults in detail, such as voltage fluctuations, shaft or bearing wear and tear, inadequate ventilation, and foundation vibration. With intrusive and non-intrusive data, the proposed FDD method is more robust and could provide more detailed diagnosis of pump faults. Full article

Background and Clinical Significance: Familial short stature is a common reason for referral in clinical genetics. While often attributed to a single genetic cause, genetic heterogeneity can complicate diagnosis and management. This report describes a family in which three distinct pathogenic variants in SHOX, PDE4D and ACAN caused overlapping phenotypes of familial short stature. Case Presentation: Clinical, radiological and molecular data were collected retrospectively at the Reference Centre for Constitutional Bone Diseases at Montpellier University Hospital. Targeted gene panels, whole genome sequencing and Sanger sequencing were employed to identify pathogenic variants. Variant interpretation followed the guidelines of the American College of Medical Genetics. A pathogenic SHOX variant (c.452G>A; p.Ser151Asn) was identified in the proband and her mother, which is consistent with dyschondrosteosis. A de novo PDE4D variant (c.671C>T; p.Thr224Ile) was identified in a cousin presenting with syndromic acrodysostosis. An ACAN splice variant (c.6833-1G>A) was detected in several family members and is associated with short stature and skeletal anomalies. An individual carrying both the SHOX and ACAN variants exhibited a more severe phenotype, suggesting an additive effect. Conclusions: This case study highlights the importance of systematic molecular investigations in families with overlapping yet heterogeneous phenotypes. Comprehensive genetic familial analysis enables personalized care and accurate genetic counselling, particularly when multiple diagnoses coexist. A family history should not preclude molecular testing, since similar phenotypes can result from different genetic causes. Full article

Understanding subsurface temperature-transition structures is essential for interpreting upper-ocean stratification; however, most existing methods rely on two-dimensional profiles and fail to resolve the full three-dimensional geometry of temperature anomalies. This study proposes the Three-Dimensional Ocean Temperature Structure Extraction method (3D-OTSE), a flexible data-driven framework that identifies coherent three-dimensional thermal-transition features directly from multi-depth ocean temperature fields. The method defines a Temperature-Contrast Index (TCI) based on local three-dimensional temperature differences, determines an adaptive threshold from the curvature of the TCI distribution, and employs 3D DBSCAN to extract volumetric structures. Rather than assuming a thermocline, 3D-OTSE detects a wide range of vertical temperature anomalies—including thermoclines, inverse thermoclines, and multilayer transitions—according to their spatial organization in the data. Applying this method to the South China Sea Basin (SCS) can reconstruct thermocline-like structures that conform to large-scale regional patterns and can also capture complex lateral variations that are difficult to detect by traditional profile diagnosis methods. The region-adaptive threshold enables this framework to adapt to inhomogeneous formation states and spatio-temporal scales. In general, 3D-OTSE provides a universal, parameter-adaptive tool for finding three-dimensional underground temperature anomaly layers, supplements perspectives for traditional methods, and lays the foundation for future multivariate and time-varying applications. Full article

Intelligent Fault Diagnosis (IFD) systems are integral to predictive maintenance and real-time monitoring but often encounter challenges such as data scarcity, non-linearity, and changing operational conditions. To address these challenges, we propose an enhanced transfer learning framework that integrates the Universal Adaptation Network (UAN) with Spectral-normalized Neural Gaussian Process (SNGP), WideResNet, and attention mechanisms, including self-attention and an outlier attention layer. UAN’s flexibility bridges diverse fault conditions, while SNGP’s robustness enables uncertainty quantification for more reliable diagnostics. WideResNet’s architectural depth captures complex fault patterns, and the attention mechanisms focus the diagnostic process. Additionally, we employ Optuna for hyperparameter optimization, using a structured study to fine-tune model parameters and ensure optimal performance. The proposed approach is evaluated on benchmark datasets, demonstrating superior fault identification accuracy, adaptability to varying operational conditions, and resilience against data anomalies compared to existing models. Our findings highlight the potential of advanced machine learning techniques in IFD, setting a new standard for applying these methods in complex diagnostic environments. Full article

Distributed acoustic sensing (DAS) technology is gaining popularity for real-time monitoring during the hydraulic fracturing of unconventional reservoirs. By transforming a standard optical fiber into a dense array of acoustic sensors, DAS provides continuous spatiotemporal measurements along the entire wellbore. Although accurate DAS-based real-time diagnosis of multistage hydraulic fracturing is critical for optimizing the efficiency of stimulation operations and mitigating operational risks in horizontal tight oil wells, existing methods often fail to provide integrated qualitative and quantitative insights. To address this gap, we present an original diagnostic workflow that synergistically combines frequency band energy (FBE), low-frequency DAS (LF-DAS), and surface injection data for simultaneous fluid/proppant allocation and key downhole anomaly identification. Field application of the proposed framework in a 47-stage well demonstrates that FBE (50–200 Hz) enables robust cluster-level volume estimation, while LF-DAS (<0.5 Hz) reveals fiber strain signatures indicative of mechanical integrity threats. The workflow can successfully diagnose sand screenout, diversion, out-of-zone flow, and early fiber failure—events often missed by conventional monitoring. By linking distinct acoustic fingerprints to specific physical processes, our approach transforms raw DAS data into actionable operational intelligence. This study provides a reproducible, field-validated framework that enhances understanding in the context of fracture treatment, supports real-time decision making, and paves the way for automated DAS interpretation in complex completions. Full article

Traditional electrical resistivity tomography (ERT) technology confronts bottlenecks such as the volume effect in the detection of termite nests in levees, while the ERT based on deep learning has insufficient interpretation accuracy due to small sample data. This study proposes an intelligent ERT diagnosis framework that integrates generative adversarial networks (GANs) with semantic segmentation models. The GAN-enhanced networks (GFU-Net and GFL-Net) are developed, incorporating a Squeeze-and-Excitation (SE) attention mechanism to suppress false anomalies. Additionally, a comprehensive loss function combining binary cross-entropy (BCE) and the Focal loss function is used to address the issue of sample imbalance. Using forward modeling based on the finite difference method (FDM), a termite nest hidden danger ERT dataset, which includes seven types of high-resistance anomaly configurations, is generated. Numerical simulations demonstrate that GFL-Net achieves a mean intersection-over-union (mIoU) of 97.68% and a spatial positioning error of less than 0.04 m. In field validation on a red clay embankment in Jiangxi Province, this method significantly improves the positioning accuracy of hidden termite nests compared to traditional least squares (LS) inversion. Excavation verification results show that the maximum error in the horizontal center and top burial depth of the termite nest identified by GFL-Net is less than 7% and 16%, respectively. The research findings provide reliable technical support for the accurate identification of termite nest hidden dangers in embankments. Full article

Reliable fault diagnosis in industrial robots is essential for minimizing downtime and ensuring safe operations. Conventional model-based methods often require detailed system knowledge and struggle with unmodeled dynamics, while purely data-driven approaches can achieve good accuracy but may not fully exploit the underlying structure of robot motion. In this study, we propose a feature-informed machine learning framework for fault detection in robotic manipulators. A multi-layer perceptron (MLP) is trained to estimate robot dynamics from joint states, and SHapley Additive exPlanations (SHAP) values are computed to derive discriminative feature representations. These attribution patterns, or SHAP fingerprints, serve as enhanced descriptors that enable reliable classification between normal and faulty operating conditions. Experiments were conducted using real-world data collected from industrial robots, covering both motor brake faults and reducer anomalies. The proposed SHAP-informed framework achieved nearly perfect classification performance (0.998 ± 0.003), significantly outperforming baseline classifiers that relied only on raw kinematic features (0.925 ± 0.002). Moreover, the SHAP-derived representations revealed fault-consistent patterns, such as enhanced velocity contributions under frictional effects and joint-specific shifts for reducer faults. The results demonstrate that the proposed method provides high diagnostic accuracy and robust generalization, making it well suited for safety-critical applications and predictive maintenance in industrial robotics. Full article

Objective. Limited evidence is available concerning the surgical outcomes of patients with congenital gastrointestinal malformations and perioperative SARS-CoV-2 infection. This study examines the scientific evidence on SARS-CoV-2 infection and congenital gastrointestinal malformations requiring surgery in children. Material and Methods. We performed a systematic review of studies reporting data on children with congenital gastrointestinal malformations and SARS-CoV-2 infection, published in international databases (PubMed and Embase) from pandemic inception up to August 2024. Studies not reporting data on the SARS-CoV-2 infection status on patients with congenital digestive malformation were excluded. We assessed the quality of the included studies according to the Joanna Institute (JBI) appraisal checklist, adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines, and registered the protocol with the PROSPERO database (CRD42024550744). Results. From the 902 titles retrieved, eight observational studies met the inclusion criteria comprising 29 patients from countries with different socioeconomic statuses. Most patients were neonates (75%) with a median age of 3 days at diagnosis and male to female ratio of 2:1. In total, 18 (62%) presented upper gastrointestinal tract anomalies, including esophageal atresia ± tracheoesophageal fistula (n = 10, 34.48%), duodenal atresia (n = 3, 10.3%), and congenital hypertrophic pyloric stenosis (n = 5, 17.2%). Lower digestive tract malformations (11, 38%) included anorectal malformations (n = 6, 20.6%), intestinal atresia (n = 3, 10.3%), Hirschsprung disease (n = 1, 3.44%), and Meckel’s diverticulum (n = 1, 3.44%). Surgeries were primarily emergency or urgent procedures and only pyloromyotomy (5/5) was consistently operated minimally invasively. SARS-CoV-2 infection was identified mainly on routine screening (>95%). Of 29 patients, 85% were discharged home, and no postoperative surgical mortality and significant complications directly associated with COVID-19 were identified, although routine postoperative morbidity not linked to SARS-CoV-2 was observed. Conclusions. Pediatric patients with congenital gastrointestinal malformationsand perioperative SARS-CoV-2 infection typically have mild illness and favorable surgical outcomes. SARS-CoV-2 positivity alone should not delay essential surgery when infection control measures are ensured. Standardized, multicenter studies are needed to clarify perioperative risks to and inform management of this high-risk group. Full article

Standard monitoring for photovoltaic (PV) systems, often based on IEC 61724-1, the standard published by the International Electrotechnical Commission (IEC) titled “Photovoltaic system performance—Part 1: Monitoring”, is frequently slow to detect critical operational anomalies, particularly those related to energy self-consumption where conventional generation-centric metrics may appear normal. This work presents a validated industrial SCADA (i.e., Supervisory Control and Data Acquisition) framework designed for the accelerated fault diagnosis of such systems. The proposed methodology leverages high-resolution, real-time visualization of specific energy-flow indicators, including the Self-Consumption Ratio (SCR) and Self-Sufficiency Ratio (SSR), to provide immediate operational intelligence. The novelty of this approach lies not in the individual parameters themselves, but in their synergistic integration into a validated, high-speed SCADA system design and real-time diagnostic methodology. The framework’s diagnostic superiority was validated on two distinct, real-world case studies in Jaén, Spain (a 2.97 kW residential and a 58.5 kW commercial system), with primary research results confirming: (1) a simulated comparative benchmarking study demonstrated a significant reduction in Mean-Time-to-Detection (MTTD), achieving a consistent diagnostic speed improvement of over 80% for critical anomalies, and (2) a 10,000 h probabilistic simulation confirmed the statistical robustness of the proposed indicators across a wide range of operating conditions. By demonstrating the practical implementation of these principles within a scalable industrial platform, this work provides a validated and reproducible technical methodology that enhances PV system diagnostics, translating performance metrics into a tangible, high-speed tool for improving operational reliability. Full article

Background and Objectives: Stratifying thyroid nodules according to malignancy risk is a crucial step in early diagnosis and patient care. Recently, deep learning techniques have emerged as powerful tools for medical diagnostics, particularly with convolutional neural networks (CNNs) applied to medical image classification. This study aimed to develop a new hybrid CNN model for classifying thyroid nodules using the TN5000 ultrasound image dataset. Materials and Methods: The TN5000 dataset includes 5000 ultrasound images, with 3572 malignant and 1428 benign nodules. To address the issue of class imbalance, the researchers applied an R-based anomaly data augmentation method and a GAN-based technique (G-RAN) to generate synthetic benign images, resulting in a balanced dataset for training. The model architecture was built on a pre-trained EfficientNet-B3 backbone, further enhanced with squeeze-and-excitation (SE) blocks and residual refinement modules to improve feature extraction. The task was to classify malignant nodules (labeled 1) and benign nodules (labeled 0). Results: The proposed hybrid CNN achieved strong performance, with an accuracy of 89.73%, sensitivity of 90.01%, precision of 88.23%, and an F1-score of 88.85%. The total training time was 42 min. Conclusions: The findings demonstrate that the proposed hybrid CNN model is a promising tool for thyroid nodule classification on ultrasound images. Its high diagnostic accuracy suggests that it could serve as a reliable decision-support system for clinicians, improving consistency in diagnosis and reducing human error. Future work will focus on clinical validation, explainability of the model’s decision-making process, and strategies for integration into routine hospital workflows. Full article

There are significant differences in the deformation patterns of different parts of arch dams, and there is a common situation of periodic data loss. To accurately analyze the deformation behavior of arch dams, this paper proposes a safety warning and anomaly diagnosis method for arch dam deformation based on the separation of environmental variable effects in different partitions and a knowledge-driven approach. This method combines various techniques such as an optimized ISODATA clustering method, probabilistic principal component analysis (PPCA), square prediction error (SPE) norm control chart, and contribution chart. By defining data forms and rules, existing engineering specifications and experience are transformed into “knowledge” and applied to the operation and management of arch dams, achieving accurate monitoring of arch dam deformation status and timely diagnosis of outliers. Through monitoring data verification of horizontal displacement in a certain arch dam partition, the results show that this method can accurately identify deformation anomalies in the arch dam and effectively separate the influence of environmental variables and noise interference, providing strong support for the safe operation of the arch dam. Accurate deformation monitoring of arch dams is essential for ensuring structural safety and optimizing operational management. However, conventional early warning indicators and empirical models often fail to capture the spatial heterogeneity of deformation and the complex coupling between environmental variables and structural responses. To overcome these limitations, this study proposes a knowledge-driven safety early warning and anomaly diagnosis model for arch dam deformation, based on spatiotemporal clustering and partitioned environmental variable separation. The method integrates the optimized ISODATA clustering algorithm, probabilistic principal component analysis (PPCA), squared prediction error (SPE) control chart, and contribution chart to establish a comprehensive monitoring framework. The optimized ISODATA identifies deformation zones with similar mechanical behavior, PPCA separates environmental influences such as temperature and reservoir level from structural responses, and the SPE and contribution charts quantify abnormal variations and locate potential risk regions. Application of the proposed method to long-term deformation monitoring data demonstrates that the PPCA-based framework effectively separates environmental effects, improves the interpretability of zoned deformation characteristics, and enhances the accuracy and reliability of anomaly identification compared with conventional approaches. These findings indicate that the proposed knowledge-driven model provides a robust and interpretable framework for precise deformation safety evaluation of arch dams. Full article