Failure Detection Research Articles

Aperiodic small signal stability method for detection and mitigation of cascading failures in smart grids

The occurrence of cascading failures poses significant risks to the stability and reliability of modern smart grids. This article presents a novel hybrid algorithm designed to assess and mitigate these failures. The technique combines advanced clustering algorithms, specifically Affinity Propagation Graph (APG) and Self-Propagating Graph (SPG), to detect critical nodes, and Unified Power Flow Controllers (UPFCs) to provide compensation to grid networks. The algorithm first uses APG to divide the network into clusters and considers the center bus as the critical node. If the center bus is not critical, SPG is applied to identify the critical node. This hybrid approach identifies the critical node in just 0.02 s (for both APG and SPG) and with improved accuracy compared to existing methods. After identifying critical nodes, UPFCs are strategically installed to regulate power flow and reduce the probability of cascading failures, with compensation taking approximately 0.14 s. Simulation results demonstrate the effectiveness of the proposed method in enhancing grid resilience and reducing the likelihood of cascading failures. By strategically deploying UPFCs at critical nodes, this approach ensures resilient grid operation in various scenarios. This research significantly contributes to the development of smart grid technologies by providing a comprehensive framework to address cascading failures in power distribution networks. The proposed method shows potential for improving the reliability and stability of power grids amid changing system dynamics and uncertainties.

DC Arc Failure Detection based on Division of Time and Frequency Components using Intelligence Models

DC Arc Failure Detection based on Division of Time and Frequency Components using Intelligence Models

Sexual orientation, gender identity and virologic failure among people with HIV: a cohort study in all of US research program

BackgroundSexual and gender minorities (SGMs) are at higher risk of HIV incidence compared to their heterosexual cisgender counterparts. Despite the high HIV disease burden among SGMs, there was limited data on whether they are at higher risk of virologic failure, which may lead to potential disease progression and increased transmission risk. The All of Us (AoU) Research Program, a national community-engaged program aiming to improve health and facilitate health equity in the United States by partnering with one million participants, provides a promising resource for identifying a diverse and large volunteer TGD cohort. Leveraging various data sources available through AoU, the current study aims to explore the association between sexual orientation and gender identity (SOGI) and longitudinal virologic failure among adult people with HIV (PWH) in the US.MethodsThis retrospective cohort study used integrated electronic health records (EHR) and self-reported survey data from the All of Us (AoU) controlled tier data, version 7, which includes participants enrolled in the AoU research program from May 31, 2017, to July 1, 2022. Based on participants’ sexual orientation, gender identity, and sex assigned at birth, their SOGI were categorized into six groups, including cisgender heterosexual women, cisgender heterosexual men, cisgender sexual minority women, cisgender sexual minority men, gender minority people assigned female at birth of any sexual orientation, and gender minority people assigned male at birth of any sexual orientation. Yearly virologic failure was defined yearly after one’s first viral load testing, and individuals with at least one viral load test > 50 copies/mL during a year were defined as having virologic failure at that year. Generalized linear mixed-effects models were used to explore the association between SOGI and longitudinal virologic failure while adjusting for potential confounders, including age, race, ethnicity, education attainment, income, and insurance type.ResultsA total of 1,546 eligible PWH were extracted from the AoU database, among whom 1,196 (77.36%) had at least one viral failure and 773 (50.00%) belonged to SGMs. Compared to cisgender heterosexual women, cisgender sexual minority women (adjusted Odds Ratio [aOR] = 1.85, 95% CI: 1.05–3.27) were at higher risk of HIV virologic failure. Additionally, PWH who were Black vs. White (aOR = 2.15, 95% CI: 1.52–3.04) and whose insurance type was Medicaid vs. Private insurance (aOR = 2.07, 95% CI: 1.33–3.21) were more likely to experience virologic failure.ConclusionsMaintaining frequent viral load monitoring among sexual minority women with HIV is warranted because it allows early detection of virologic failure, which could provide opportunities for interventions to strengthen treatment adherence and prevent HIV transmission. To understand the specific needs of subgroups of SGMs, future research needs to examine the mechanisms for SOGI-based disparities in virologic failure and the combined effects of multi-level psychosocial and health behavior characteristics.

OffloaD: Detection Failure-based Scheduler for Offloading Object Detection

The current times ask for resource-constrained devices such as drones, light mobile robots, XR glasses, or mobile phones to perform object detection efficiently and in real time. However, when executed on the device, object detection fails to achieve the accuracy requirements. In addition, depending on a powerful edge to offload computation is time-consuming as it requires the transmission of a large amount of data and limits the usage in degraded network conditions. In this paper, we propose OffloaD, a novel offloading scheduler based on the detection of failures for object detection. The method addresses the problem of offloading between a resource-constrained device and an edge by leveraging the accuracy of the object detection model at the device and the network conditions. Accuracy is considered through a detection failure metric that consists of introspective, golden and latency scores. Our main contribution is the offloading scheduler based on the detection of failures of the object detector, which reduces unnecessary data transmission without accuracy degradation. Our method demonstrates that offloading to expensive detectors will not always increase performance and enables detection results and failure estimations even if the network is degraded or unavailable. We implement OffloaD in a physical testbed and perform extensive comparisons against several baselines. The experiments suggest that our approach improves the overall precision-latency performance by reducing the end-to-end latency between 18% and 37% in ideal network conditions and a low latency increase in degraded network conditions while obtaining an accuracy with minimum degradation compared to the baselines. In addition, comparisons to related work methods demonstrate the state-of-the-art performance of OffloaD.

Early Detection of Heart Failure with Autonomous AI-Based Model Using Chest Radiographs: A Multicenter Study.

The opportunistic use of radiological examinations for disease detection can potentially enable timely management. We assessed if an index created by an AI software to quantify chest radiography (CXR) findings associated with heart failure (HF) could distinguish between patients who would develop HF or not within a year of the examination. Our multicenter retrospective study included patients who underwent CXR without an HF diagnosis. We included 1117 patients (age 67.6 ± 13 years; m:f 487:630) that underwent CXR. A total of 413 patients had the CXR image taken within one year of their HF diagnosis. The rest (n = 704) were patients without an HF diagnosis after the examination date. All CXR images were processed with the model (qXR-HF, Qure.AI) to obtain information on cardiac silhouette, pleural effusion, and the index. We calculated the accuracy, sensitivity, specificity, and area under the curve (AUC) of the index to distinguish patients who developed HF within a year of the CXR and those who did not. We report an AUC of 0.798 (95%CI 0.77-0.82), accuracy of 0.73, sensitivity of 0.81, and specificity of 0.68 for the overall AI performance. AI AUCs by lead time to diagnosis (<3 months: 0.85; 4-6 months: 0.82; 7-9 months: 0.75; 10-12 months: 0.71), accuracy (0.68-0.72), and specificity (0.68) remained stable. Our results support the ongoing investigation efforts for opportunistic screening in radiology.

Detection of Contamination and Failure in the Outer Race on Ceramic, Metallic, and Hybrid Bearings through AI Using Magnetic Flux and Current

Bearings are one of the most essential elements in an induction motor, and they are built with different materials and constructions according to their application. These components are usually one of the most failure-prone parts of an electric motor, so correct and accurate measurements, instrumentation, and processing methods are required to prevent and detect the presence of different failures. This work develops a methodology based on the fusion of current and magnetic stray flux signals, calculation of statistical and non-statistical indicators, genetic algorithms (GAs), linear discriminant analysis (LDA), and neural networks. The proposed approach achieves a diagnostic effectiveness of 99.8% for detecting various damages in the outer race at 50 Hz frequency and 96.6% at 60 Hz. It also demonstrates 99.8% effectiveness for detecting damages in the presence of contaminants in lubrication at 50 Hz and 97% at 60 Hz. These results apply across metallic, ceramic, and hybrid bearings.

Towards Proactive Heart Health: A Machine Learning - Powered Approach for Chronic Heart Failure Detection

Towards Proactive Heart Health: A Machine Learning - Powered Approach for Chronic Heart Failure Detection

Urea adsorption and detection using silicon nanowires doped with B, Al, C, Ge, N, and P: A DFT investigation

Urea can serve as a biomarker for the detection of various illnesses, including renal and hepatic failure. Consequently, the development of novel devices and materials capable of adsorbing and identifying urea is a crucial objective for the scientific community. This study theoretically investigates the adsorption and detection capabilities of doped silicon nanowires (SiNWs) for urea using Density Functional Theory (DFT). Doping involves substituting a silicon atom on the surface with a dopant atom; B, Al, C, Ge, N, and P were employed for this purpose. This study presents an innovative method for enhancing urea adsorption and detection by doping SiNWs with group XIII elements, specifically aluminum and boron atoms. The results indicate that this doping significantly improves urea adsorption on SiNWs compared to undoped SiNWs. Notable changes in the bandgaps and work functions of the doped nanowires following urea adsorption suggest their potential use as diagnostic tools for uremia.

Chemiluminescence immunoassay amplified by functional acridinium ester for the detection of NT-proBNP in clinical samples

High sensitivity and accuracy of heart failure biomarker, N-terminal prohormone brain natriuretic peptide (NT-proBNP), is critical for the early detection of heart failure. However, traditional chemiluminescent (CL) probe still face the challenges of poor solubility, in particular, significant disappointing bioavailability, which profoundly limit its clinical applications. In this paper, we demonstrate functional CL probe acridinium ester (AE-2) for the detection of NT-proBNP, through introducing a hydrophilic unit for ensuring high water dispersity and reactive group for promoting better bioavaiability with antibody. Compared to the traditional CL probe, the functional CL probe AE-2 exhibits extraordinary features, such as better biocompatibility, higher label rate with antibody, as well as significant chemiluminescence signal. In addition, this CL probe exhibits excellent linearity with the test data of Longhua Hospital, suggesting that AE-2 could be used as a clinical examination assay to detect NT-proBNP in real clinical samples. Thus, this functional CL probe AE-2 can be a powerful tool for elucidating the roles of NT-proBNP in clinical samples.

Use of a refueling machine mast sipping system to detect leaking FAs during operational fuel cladding failure detection

Use of a refueling machine mast sipping system to detect leaking FAs during operational fuel cladding failure detection

How effective is our current follow-up for patients with hypopharyngeal carcinoma?

The study aimed to evaluate the diagnostic efficacy of routine follow-up in detecting recurrent disease in hypopharyngeal carcinoma. Data from 76 patients with a total of 620 follow-up visits, 367 radiological studies, and 126 panendoscopies were retrospectively assessed regarding the diagnosis of recurrent disease based on the results of (I) radiological studies, (II) clinical examinations, and (III) clinical symptoms. All locally relapsed patients became symptomatic, and new onset of dysphagia (p < 0.001) was the most frequent complaint. The sensitivity for detecting local recurrences was 100% for both patients' symptom assessments and clinical examinations. The highest overall accuracy was found for clinical examinations (93.4%), followed by symptom assessments (80.5%), and radiological studies (73.8%). The risk of false positive radiological reports and subsequent panendoscopies was 2.2 times higher after previous radiotherapy (32.0% vs. 14.6%; p < 0.001). Symptom assessments and clinical examinations are highly efficient for detection of local failures.

Predictive Modeling of Conveyor Belt Deterioration in Coal Mines Using AI Techniques

Conveyor belts are vital for material transportation in coal mines due to their cost-effectiveness and versatility. These belts endure significant wear from harsh operating conditions, risking substantial financial losses if they fail. This study develops five artificial neural network (ANN) models to predict conveyor belt damage using 11 parameters from the Belchatow brown coal mine in Poland. The models target five outputs: number of repairs and cable cuts, cumulative number of repairs and cable cuts, and their ages. Various optimizers (Adam, Nadam, RMSprop, Adamax, and stochastic gradient descent or SGD) and activation functions (ReLU, Swish, sigmoid, tanh, Leaky ReLU, and softmax) were tested to find the optimal configurations. The predictive performance was evaluated using three error indicators against actual mine data. Superior models can forecast belt behavior under specific conditions, aiding proactive maintenance. The study also advocates for the Diagbelt+ system over human inspections for failure detection. This modeling approach enhances proactive maintenance, preventing total system breakdowns due to belt wear.

Assessment of left atrial function provides incremental value: the left atrial volumetric/mechanical coupling index in patients with chronic kidney disease.

Heart failure is a common cause of adverse cardiovascular outcomes in patients with chronic kidney disease (CKD). Left atrial (LA) characteristics are thought to be involved in the development of heart failure. However, LA assessment is complex. Though a variety of parameters have been defined, there is no single parameter that best defines LA function. Pilot data indicate that left atrial volumetric/mechanical coupling index (LACI) may be useful, but data with CKD are lacking. The objective of this study was to define LACI in a cohort of patients with CKD and to assess its value in evaluating LA function and predicting heart failure. A cohort of patients with CKD was enrolled at our hospital between 2021 and 2023. Follow-up was performed for heart failure. LACI is a volumetric to mechanical coupling index, calculated as the ratio of the LA volume index to the tissue-Doppler myocardial velocity at atrial contraction. Spearman's rank correlation or Pearson's correlation was used to calculate the correlation between LACI and echocardiographic/hemodynamic variables. Receiver operating characteristic curve (ROC) analysis was utilised to derive the area under the curve (AUC) for LACI, LVGLS, LASr, LASct and LASI for the detection of heart failure. Kaplan-Meier survival curves were employed to compare clinical outcomes based on LACI thresholds. A multivariable logistic regression analysis was employed to assess the relationship between risk factors and elevated LACI. Cox proportional hazards regression was used to identify risk factors for heart failure. LACI showed a positive correlation with NT-proBNP, CK-MB, LAVI, E/e' and LASI (r = 0.504, 0.536, 0.856, 0.541 and 0.509, p < 0.001); and a negative correlation with LASr (r = -0.509, p < 0.001). On the ROC analysis for the determination of heart failure, the AUC of LACI was comparable to those of LVGLS (0.588 vs. 509, p = 0.464), LASr (0.588 vs. 0.448, p = 0.132), LASct (0.588 vs. 0.566, p = 0.971) and LASI (0.588 vs. 0.570, p = 0.874). The cardiovascular risk factors increased by LACI were age, BMI, diabetes, triglycerides, LA size, LASr, LASI, E/A, E/e' and EF (p < 0.05). During a median follow-up of 16 months (range, 6-28 months), the event-free survival curves demonstrated a higher risk of heart failure in the group with LACI > 5.0 (log-rank test: P < 0.001). LACI > 5.0 was an independent predictor of heart failure [OR: 0.121, 95% CI (0.020-0.740), p = 0.022]. LACI may prove to be a valuable tool for assessing LA function in patients with CKD, and could be integrated into the routine assessment of LA for the purpose of prognostic assessment and clinical decision-making in patients with CKD.

Evaluation of the Transfemoral Bone-Implant Interface Properties Using Vibration Analysis.

Evaluating the bone-implant interface (BII) properties of osseointegrated transfemoral (TFA) implants is important for early failure detection and prescribing loads during rehabilitation. The objective of this work is to derive and validate a 1D finite element (FE) model of the Osseointegrated Prosthetic Limb (OPL) TFA system that can: (1) model its dynamic behaviour and (2) extract the BII properties. The model was validated by: (1) comparing the 1D FE formulation to the analytical and 3D FE solutions for a simplifiedcylinder, (2) comparing the vibration modes of the actual TFA geometry using 1D and 3D FE models, and (3) evaluating the BII properties for three extreme conditions (LOW, INTERMEDIATE, and HIGH) generated using 3D FE and experimental (where the implant was embedded, using different adhesives, in synthetic femurs) signals for additional validation. The modes predicted by the 1D FE model converged to the analytical and the 3D FE solutions for the cylinder. The 1D model also matched the 3D FE solution with a maximum frequency difference of 2.02% for the TFA geometry. Finally, the 1D model extracted the BII stiffness and the system's damping properties for the three conditions generated using the 3DFE simulations and the experimental INTERMEDIATE and HIGH signals. The agreement between the 1D FE and the 3D FE solutions for the TFA geometry indicates that the 1D model captures the system's dynamic behaviour. Distinguishing between the different BII conditions demonstrates the 1D model's potential usefor the non-invasive clinical evaluation of the TFA BII properties.

Finite element data-driven deep learning-based tensile failure analysis of precast bridge slab joint

The joint is a critical component that facilitates force transmission across precast bridge slabs while maintaining structural integrity. Finite Element Analysis (FEA) is commonly used to examine the mechanical properties and load-bearing capacity of joints with different configurations. However, the repetitive process of “modeling-meshing-computing” consumes substantial manpower, time, and expertise. This study proposes an approach consisting of three key steps: (1) predicting strain distributions, (2) determining failure modes, and (3) analyzing interpretability of predicted results. Specifically, DeepLabv3+ is used to map the Finite Element (FE) model to strain distributions. A binary classification model based on VGG-16 predicts the failure state of the specimen. Interpretability analysis, using Gradient-weighted Class Activation Mapping (Grad-CAM), provides insights into the neural network’s decision-making process. The optimized models achieve a Mean Intersection over Union (MIoU) of 0.5691 for strain distribution prediction on the cross-sectional surface and 0.6424 on the front surface. The failure detection model achieves an F1 score of 0.992 in determining failure states. The interpretability results align well with fracture mechanics theory, enhancing confidence in the model’s predictions. This method serves as a surrogate to reduce the workload of finite element analyses, facilitating strain analysis and failure detection.

Clinical and Radiological Characteristics of Respiratory Tuberculosis Treatment Failures in Children and Adolescents and Timing of their Detection Depending on Examination Method

The objective: selection of optimal timing for chest CT to identify respiratory tuberculosis treatment failures in children and adolescents through clinical and radiological comparison.Subjects and Methods. A retrospective comparative study was conducted. Totally 70 patients aged from 2 to 17 years with various clinical forms of respiratory tuberculosis were examined. Retrospectively, patients were divided into 3 groups: Group 1 (35 people) – CT was performed before the treatment start and, as the first control, after 2-3 months of chemotherapy; Group 2 (17 people) had a CT before the chemotherapy start and after 2-3 months – plain X-ray (or in combination with linear tomography), CT control – after 6-9 months after the chemotherapy start; Group 3 (18 people) included patients who had only plain X-ray (or in combination with linear tomography) before and during chemotherapy, CT was first performed after treatment failureResults. In all groups, in more than 40% of cases, radiological changes were the only informative criterion of treatment failure. CT performed in the early stages was optimal for timely detection of treatment failure: before the chemotherapy start and in 2-3 months (Group 1). In patients of Groups 2 and 3, opportunities for timely correction of chemotherapy were missed due to inadequate assessment of pathological changes. CT performed after 6-9 months of treatment reliably revealed irreversible changes in 53% and 78% (Groups 2 and 3), respectively.

An Intelligent Manufacturing Management System for Enhancing Production in Small-Scale Industries

Industry 4.0 integrates the intelligent networking of machines and processes through advanced information and communication technologies (ICTs). Despite advancements, small mechanical manufacturing enterprises face significant challenges transitioning to ICT-supported Industry 4.0 models due to a lack of technical expertise and infrastructure. These enterprises commonly encounter variable production volumes, differing priorities in customer orders, and diverse production capacities across low-, medium-, and high-level outputs. Frequent issues with machine health, glitches, and major breakdowns further complicate optimizing production scheduling. This paper presents a novel production management approach that harnesses bio-inspired methods alongside Internet of Things (IoT) technology to address these challenges. This comprehensive approach integrates the real-time monitoring and intelligent production order distribution, leveraging advanced LoRa wireless communication technology. The system ensures efficient and concurrent data acquisition from multiple sensors, facilitating accurate and prompt capture, transmission, and storage of machine status data. The experimental results demonstrate significant improvements in data collection time and system responsiveness, enabling the timely detection and resolution of machine failures. Additionally, an enhanced genetic algorithm dynamically allocates tasks based on machine status, effectively reducing production completion time and machine idle time. Case studies in a screw manufacturing facility validate the practical applicability and effectiveness of the proposed system. The seamless integration of the scheduling algorithm with the real-time monitoring subsystem ensures a coordinated and efficient production process, ultimately enhancing productivity and resource utilization. The proposed system’s robustness and efficiency highlight its potential to revolutionize production management in small-scale manufacturing settings.

Automated Echocardiographic Detection of Heart Failure With Preserved Ejection Fraction Using Artificial Intelligence Is Associated With Cardiac Mortality and Heart Failure Hospitalization

Automated Echocardiographic Detection of Heart Failure With Preserved Ejection Fraction Using Artificial Intelligence Is Associated With Cardiac Mortality and Heart Failure Hospitalization

The importance of ward-based education in the postoperative assessment of free flaps

Background: Despite the theoretical and practical advantages of adjunct-based free flap failure detection, the introduction of ultrasonographic techniques has not changed free flap failures rates and their use remains secondary to clinical assessment. Our study aims to develop an educational framework to improve the confidence of nursing staff caring for free flaps, with an emphasis on early detection of flap failure, thereby improving postoperative outcomes. Methods: Nursing staff in intensive care units and surgical wards caring for free flaps completed standardised questionnaires either before or after an educational seminar detailing clinical and Doppler assessment of free flaps. Differences between cohorts were summarised descriptively with frequencies and percentages. Overall confidence scores for pre-education and post-education cohorts were compared. Results: Overall, 173 responses were collected. Most staff were from intensive care units (n = 114) and almost one-third completed surveys following the educational intervention (n = 51). Microsurgical ward staff had more experience in the postoperative care of free flaps than intensive care unit staff (29% vs 18% had experience caring for &gt; 50 free flap cases). Confidence in assessing a free flap by clinical, implantable Doppler or external Doppler ultrasound was universally increased among ward staff compared to intensive care unit staff (p ≤ 0.007). Altogether, 51 per cent of intensive care unit staff had never identified a failing flap compared with 35 per cent of ward staff. Education increased confidence in managing and escalating failing flaps unanimously. Most failing flaps were identified by clinical assessment alone or combined with adjunct-based methods (94%). Conclusion: This study demonstrates that the provision of targeted education for nursing staff, both in intensive care units and on surgical wards, is useful for increasing confidence in the clinical assessment and early detection of free flap compromise. Increased confidence and improved clinical assessment may allow for early intervention and improved salvage rates for a threatened free flap, thereby improving patient outcomes.

Current sensor unanticipated fault detection and compensation of 14.5-meter optical/infrared telescope drive system based on algebraic transformations

This paper discusses an unanticipated fault detection, isolation, and compensation (FDIC) strategy for the arc splicing permanent magnet synchronous motor (PMSM) operating under a promising 14.5-meter optical/infrared telescope drive system, specifically focusing on current sensors. The application is based on algebraic transformations that allow not only failure detection but also location and isolation. Besides, detection is performed merely by means of the measured current sensors and does not require additional knowledge or estimators; isolation utilizes information provided by detection to locate where the faults originated and release isolation signals; and compensation is carried out through the remaining currents that are not affected by the faults. It is noted from the results that the performance of FDIC in faulty mode is very acceptable and uncompromising in terms of fault detection, current symmetry, speed tracking, load torque and robustness remedial measures. Therefore, the proposed method can effectively ensure the stable operation of the drive system in the presence of faults.