Reliability Challenges Research Articles

Future-Proof Networks: NFV’S Role In 5g and Beyond

Riding the wave of the multifaceted rapid change in communication technologies and universal deployment of 5G, Network Functions Virtualization, or NFV, has emerged as a disruptive framework for adaptable, scalable, and cost-efficient networks. This can be because NFV decouples network functions from specialized hardware, affording the flexibility that addresses growing demands in ultra-reliable low-latency communications, enhanced mobile broadband, and massive machine-type communications. These capabilities are essential for meeting 5G’s requirements and preparing for the more complex challenges anticipated with 6G. As 6G transition begins, the role of NFV has to be reassessed in order to meet the increased challenges for ultra-low latency, massive connectivity, and unprecedented network reliability. This paper considers the ability of NFV to evolve with new networking standards by unpacking its potentials, limitations, and opportunities for improvement. This research determines factors critical to the scalability, flexibility, and efficiency of NFV in various network scenarios through simulations, case studies, and performance metric analyses. In doing this, it raises the need for advancing NFV architecture to stay relevant. Rather than only looking at current applications, this work addresses new future needs and maybe even answers issues for future stringent network standards that do not compromise on profitability. Such insights will be useful for developing future-proof infrastructures, positioning NFV as a cornerstone in nextgeneration networks such as 6G and beyond.

Predicting aging of IGBT solder layer using saturation voltage approach with CPO-SVR data modeling

Addressing the criticality of IGBT modules in power electronics and reliability challenges in industrial applications, this study focuses on solving the important problem of solder layer aging failure in IGBT modules. Under defined operating conditions, a specific set of electrical parameters, notably the collector-emitter saturation voltage Vce(sat), serves as a critical indicator for monitoring the degradation of solder layers within IGBT modules. This parameter is indicative of potential solder layer aging failures, which can be identified and studied to predict and mitigate such failures effectively. In order to analyze the aging trend more deeply and accurately, a new nature-inspired meta-heuristic algorithm based on Crested Porcupine Optimizer is introduced. Combining multiple data during the operation of the IGBT module, such as gate voltage, gate current, heatsink temperature, case temperature, collector-emitter current, and Vce(sat), the parameter optimization is carried out by using CPO, and a prediction model based on the optimization of the SVR by CPO is established, which is successfully implemented to predict the accurate prediction of IGBT module solder layer lifetime. The study also validates the effectiveness of the CPO-SVR method on an accelerated aging dataset of IGBT modules provided by NASA Lab. The results of this research contribute to an in-depth understanding and prediction of the aging process of IGBT modules, which in turn improves the reliability and lifetime of the devices.

Artificial Intelligence in Business Scenario Analysis: A Framework for Enhanced Decision-Making Through What-If Simulations

This article examines the transformative impact of artificial intelligence on scenario analysis and what-if simulations in business analytics, addressing a critical gap in current literature regarding the integration of AI-driven predictive modeling with traditional business planning methodologies. Through a systematic analysis of implementation cases across financial services and e-commerce sectors, the article demonstrates how AI-enhanced simulation models significantly improve the speed, accuracy, and adaptability of scenario planning processes. The findings indicate substantial improvements in analysis time and prediction accuracy compared to traditional methods, particularly in areas of pricing optimization, risk assessment, and stress testing. The article synthesizes data from multiple enterprise implementations to develop a comprehensive framework for AI integration in scenario analysis, addressing key challenges in data quality, model reliability, and real-time processing capabilities. Results suggest that organizations leveraging AI-driven scenario analysis demonstrate enhanced capability in anticipating market fluctuations, optimizing resource allocation, and responding to environmental changes, though implementation success is heavily dependent on data infrastructure maturity and organizational readiness. This article contributes to both theoretical understanding and practical application of AI in business analytics, providing actionable insights for practitioners while identifying critical areas for future research in the evolving landscape of intelligent business simulation technologies.

Optimizing seismic-based reservoir property prediction: a synthetic data-driven approach using convolutional neural networks and transfer learning with real data integration

Reservoir characterization through seismic data analysis is essential for exploration and production in the petroleum industry. However, seismic-to-well tie discrepancies, limited availability of high-quality well data, and resolution constraints pose a reliability challenge. While previous studies offer valuable insights, they still struggle to achieve high-resolution predictions in a complex geologically environment given high reliance on well data. This study integrates synthetic data-driven techniques with real data, including convolutional neural networks (CNN) and transfer learning, to improve seismic reservoir characterization. We utilize nearby well statistics and a rock physics model (RPM) to simulate pseudo wells representing various geological scenarios. Synthetic seismic gathers are generated from these pseudo wells, which are based on RPM and local well control, to train the CNN. Transfer learning is then applied to adapt the CNN to better distinguish between real and synthetic data, enhancing reservoir predictions. A comparative analysis of P-impedance predictions from three methodologies: theory-driven Pre-Stack-Seismic-Inversion (TDSI), Deep-Neural-Network (DNN), and our CNN approach, showed that CNN achieved nearly 97% prediction accuracy with low error rates, compared to relatively lower prediction accuracy rates of DNN (86.2%) and TDSI (81.5%) with high error rates, according to robust metrics including R-square, RMSE, MSE, and MAE. These results indicate that CNN not only enhanced resolution but also closely aligned with well data and superior lateral continuity, even in blind well scenarios. This study effectively integrates synthetic data-driven techniques with CNNs and transfer learning to advance seismic reservoir property prediction, offering a robust solution to overcome limitations in traditional and DNN-based approaches.

Charging Ahead: The Evolution and Reliability of Nickel‐Zinc Battery Solutions

ABSTRACTNickel‐Zinc (Ni‐Zn) batteries offer an interesting alternative for the expanding electrochemical energy storage industry due to their high‐power density, low cost, and environmental friendliness. However, significant reliability challenges such as capacity fading, self‐discharge, thermal instability, and electrode degradation detract from their competitiveness in the market, hindering their widespread adoption. This study thoroughly examines the degradation mechanisms and approaches to improve the reliability of Ni‐Zn batteries: Starting with their basic chemistry, operating principles, and degradation pathways, strategies for improvement are explored including material modification, electrolyte optimization, cell design approaches, and thermal management techniques. Advanced characterization methods for data collection and reliability assessment are discussed, including electrochemical, structural, spectroscopic, and in situ techniques which are noted for their ability to identify key areas of concern for this cell chemistry. We further consider emerging trends such as novel materials, hybridization with other energy technologies, and the challenges of large‐scale implementation, emphasizing the need for standardized reliability testing protocols. Opportunities for the integration of advanced sensing, such as fiber Bragg grating (FBG) sensors for real‐time monitoring and anomaly detection, along with machine learning (ML) and prognostics and health management of Ni‐Zn batteries are highlighted, as these open the door to future research directions. This comprehensive review should serve as a resource for researchers, engineers, and industry experts aiming to advance and commercialize dependable, high‐performing Ni‐Zn battery technology for a sustainable energy future. image

UV‐Induced Degradation of Industrial PERC, TOPCon, and HJT Solar Cells: The Next Big Reliability Challenge?

With the surge of UV‐transparent module encapsulants in the photovoltaic industry aiming to boost quantum efficiency, modern silicon solar cells must now inherently withstand UV exposure. UV‐induced degradation (UVID) of nonencapsulated laboratory and industrial solar cells from several manufacturers is investigated. Passivated emitter rear contact (PERC), tunnel oxide passivating contact (TOPCon), and silicon heterojunction (HJT) cells can suffer from severe implied voltage degradation (>20 mV) after UV exposure relating to 3.8 years of module installation in the Negev desert. Front UV‐exposure causes more performance loss than an equal rear dose. This is connected to a higher UV transmission of the cell layers outside the bulk, indicating the photons need to reach the silicon surface to induce damage. Current–voltage measurements of the TOPCon groups most sensitive to UV degradation show more than 7%rel efficiency loss with the Voc as the main contributor. For two TOPCon groups, dark storage for 14 days after UV exposure causes an additional voltage drop on a similar scale as the UV damage itself, impeding straightforward reliability testing. UVID appears to be a complex process general to all dominant cell architectures with the potential to diminish efforts in efficiency optimization within only a few years of field employment.

A Review of Analytical and Intelligent Methods for Optimal Distributed Generation Placement in Modern Distribution Networks: Techniques, Challenges, and Future Directions

This comprehensive review examines the state-ofthe-art methodologies for optimal Distributed Generation (DG) placement in modern distribution networks, addressing the critical challenges of power quality enhancement and system reliability. The study systematically analyses various approaches including analytical methods, optimization techniques, artificial intelligence algorithms, graph theory applications, simulation-based methods, and hybrid solutions. Recent research indicates that optimized DG placement can achieve up to 60% reduction in system losses and 15% improvement in voltage profiles. The review highlights that while traditional analytical methods provide mathematically robust solutions, they often face scalability challenges in large networks. Artificial intelligence methods, particularly hybrid approaches combining neural networks with conventional techniques, have demonstrated superior performance with 25% better solution quality and 35% reduced computational time. The paper also explores emerging trends, including real-time optimization and blockchain integration, while addressing critical challenges such as renewable energy intermittency and regulatory frameworks. This review contributes to the field by providing a comprehensive evaluation framework for DG placement methods and identifying promising directions for future research in the context of evolving smart grid technologies.

Hallucination or vision? Establishing reliability and validity of AI-based qualitative analysis of the unified model of activism in the Scottish independence Twitter debate

PurposeThe present study explores both the validation of the unified model of activism and the methodological reliability of the LlamaParsing approach to natural language processing. Theoretically, it applies the unified model of activism within the context of the Scottish independence movement, evaluating its effectiveness in social media environment.Design/methodology/approachMethodologically, it addresses the reliability and validity challenges associated with AI analyses, particularly the issue of AI hallucinations—instances where AI generates seemingly accurate but incorrect information. By employing the LlamaParsing approach and then comparing and contrasting it with a quantitative content coding process, the study demonstrates how context-specific instructions can enhance the accuracy of AI analyses.FindingsThe findings indicate this approach not only tests and extends the unified model of activism but also offers a robust methodological framework for using NLP and RAG in qualitative research. This dual focus underscores the potential of AI to provide systematic and theoretically valuable insights while highlighting the importance of mitigating its limitations.Originality/valueThis study represents a cutting-edge approach to qualitative data analysis, theory development, and theory testing in communication using a tool that was developed in 2024.

Survey of Reliability Challenges and Assessment in Power Grids with High Penetration of Inverter-Based Resources

Decarbonization is driving power systems toward more decentralized, self-governing models. While these technologies improve efficiency, planning, operations, and reduce the carbon footprint, they also introduce new challenges. In modern grids, particularly with the integration of power electronic devices and high penetration of Renewable Energy Sources (RES) and Inverter-Based Resources (IBRs), traditional reliability concepts may no longer ensure adequate performance due to systemic restructuring. This shift necessitates new or significantly modified reliability indices to capture the characteristics of the evolving power system. Ensuring converter reliability is essential for effective planning, which requires precise, component-to-system-level modeling, as different converters impact system performance indicators. However, the existing literature in this field faces a significant limitation, as most studies focus on a singular perspective. Some examine reliability at the device-level, others at the component-level, while broader reviews in power systems often emphasize system-level analysis. In this paper, we aim to bridge these gaps by comprehensively reviewing the interconnections between these levels and analyzing the mutual influence of power converter and system reliability. A key point to highlight is that, with the rapid evolution of modern power grids, decision-makers must adopt a multi-level approach that incorporates insights from all levels to enable more accurate and realistic planning and operational strategies. Our ultimate goal is to provide an in-depth investigation of studies addressing the unique challenges posed by modern power grids. Finally, we will highlight the gaps in the literature and suggest directions for future research.

Applications, Risk, Challenges, and Future Prospects of ChatGPT in Electronic Records Management

The widespread application and rapid development of ChatGPT are disrupting traditional models across industries, bringing revolutionary changes to electronic records management. ChatGPT can be applied in the following scenarios within electronic records management: intelligent classification and archiving, automatic summarization and information extraction, knowledge-based question answering, information retrieval, and semantic analysis with association building. Meanwhile, attention should be given to the following risks and challenges: data privacy and security concerns, legal and ethical issues, and technical sustainability and reliability challenges. Looking forward, the application of ChatGPT in electronic records management is expected to trend toward focusing on core archival areas, innovating record management methods, strengthening regulatory compliance, and enhancing personnel’s data literacy.

Electromigration in Cu–Cu joints: Measurement of activation energy and polarity effect

Electromigration (EM) has been a pivotal reliability challenge for interconnects, but there are only few studies on EM in Cu–Cu joints. In this work, accelerated EM tests for the Cu–Cu joints were conducted at three temperatures (150 °C, 179 °C, and 208 °C) and the temperature-dependent failures were discussed. The polarity effect of the grain growth behaviors at the bonding interfaces was observed at the opposite electron flows, which may be attributed to the difference in EM-build-up stress conditions and the divergence in atomic fluxes. Moreover, the activation energy (Ea) extrapolated from the three EM temperatures was derived as 0.9 eV. As compared to the solder joints with same dimensions, the Cu–Cu joints possess more than ten times of time-to-failures (TTFs) under the same EM condition.

Research on single-phase grounding detection method in small-current grounding systems based on image recognition

In small-current grounding systems, the fault current during a single-phase grounding fault is small, and the transient process is complex, leading to challenges in line selection accuracy and reliability. This paper proposes a single-phase grounding line selection method based on transfer learning using the ResNet-50 model. Zero-sequence voltage and current waveforms at the fault moment are preprocessed and combined to generate training data for the model. Simulation results using PSCAD demonstrate that the proposed method achieves 99.77% validation accuracy, even under noisy conditions. These results confirm the method’s feasibility and reliability in identifying grounding faults in various conditions.

Lumbar Radicular Pain in the Eyes of Artificial Intelligence: Can You ‘Imagine’ What I ‘Feel’?

Pain is a complex sensory and emotional experience that significantly impacts individuals' well-being. Lumbar radicular pain (LRP) is a prevalent neuropathic pain affecting 9.9% to 25% of the population annually.Accurate identification of pain patterns in LRP is essential for diagnosis and management. Artificial intelligence has potential in health care but faces challenges in reliability and accuracy. This study aimed to investigate the accuracy and consistency of LRP patterns demonstrated by ChatGPT-4o. The study was conducted at Üsküdar State Hospital from June 1 to June 30, 2024, utilizing the Generative Pretrained Transformer (GPT), version 4o language model. ChatGPT-4o was prompted to generate and mark LRP patterns for L4, L5, and S1 radiculopathies on an anatomical model. The process was repeated after two weeks to assess consistency. The markings by ChatGPT were compared with those by two experienced specialists using OpenCV for analysis. ChatGPT's initial and follow-up markings of L4, L5, and S1 radiculopathy pain patterns were statistically significantly different from each other and from the specialists' markings (P < 0.001 for all comparisons). ChatGPT currently lacks the capacity to accurately and consistently represent LRP patterns. AI tools in health care require further refinement, validation, and regulation to ensure reliability and safety. Future research should involve multiple AI platforms and broader medical conditions to enhance generalizability.

Design and Architecture Definition for Advanced 3D Fan-Out Wafer-Level Packaging

Advanced 2.5D/3D wafer-level fan-out packaging technologies have been studied widely in literature and industry in recent years. Miniaturization, reduction in manufacturing costs, improvement in performance, and lower power consumption using packaging technologies drive increase in interconnect density and pitch scaling. Heterogeneous systems in package used in advanced packaging often include a combination of silicon, epoxy underfill or mold compounds, and organic or inorganic passivation or redistribution layers. This heterogeneity poses challenges with manufacturability, process selection, package architecture, and test vehicle design. The coefficient of thermal expansion mismatch, wafer warpage, and wafer yield are some of the critical process challenges. Delamination of silicon side wall or passivation to epoxy underfill or mold compound, joint fatigue are critical reliability challenges. This article explores various processes and reliability challenges with 3D wafer-level fan-out packaging and provides package design and architecture guidelines to overcome these failure modes. Stacked die-to-wafer test vehicles have been used for data collection. Different assembly materials, processes, and tooling have been evaluated to define comprehensive design rules.

Wearable Sensors for Healthcare of Industrial Workers: A Scoping Review

Background and Objectives: This scoping review evaluates the use of wearable sensor technologies for workplace safety and health monitoring in industrial settings. The aim is to synthesize evidence on the impact of these sensors and their application in high-risk environments. Materials and Methods: Following the PRISMA guidelines, a systematic search across four international electronic databases yielded 59 studies, of which 17 were included in the final review. The selection criteria involved studies that specifically utilized wearable sensors to monitor various health and environmental parameters relevant to industrial workers. Results: The analysis categorizes wearable technologies into five distinct groups based on their function: gas monitoring technologies, heart rate and physiological data collection, fatigue and activity monitoring, comprehensive environmental and physiological monitoring, and advanced sensing and data collection systems. These devices demonstrated substantial benefits in terms of early detection of health risks and enhancement of safety protocols. Conclusions: The review concludes that wearable sensor technologies significantly contribute to workplace safety by providing real-time, data-driven insights into environmental hazards and workers’ physiological status, thus supporting proactive health management practices in industrial settings. Further research is recommended to address the challenges of data privacy, sensor reliability, and cost-effective integration to maximize their potential in occupational health safety.

Addressing the reliability challenge: Subsidy policies for promoting renewable electricity consumption

The intermittency and volatility of renewable electricity pose challenges to supply reliability, which is not conducive to renewable energy consumption. To ensure a reliable electricity supply, more governments implement subsidy policies to promote the adoption of innovative technologies by renewable energy producers to enhance supply reliability. We compare two types of subsidies provided by a government: investment subsidy (IS) policy, which is implemented in the deployment stage to directly reduce improvement costs, and operational subsidy (OS) policy, which is implemented in the operational stage to increase the renewable energy producer’s marginal returns. First, we show that without government intervention, customers’ low green consciousness or higher improvement costs may prevent the renewable energy producer from enhancing supply reliability. Second, through a comprehensive comparison, we find that both subsidy policies can incentivize the renewable energy producer to improve supply reliability when customers are more green-conscious, and the improvement cost is high. However, the OS policy and the IS policy operate on different mechanisms: the IS policy can directly alleviate the improvement cost burden on the renewable energy producer, while the OS policy serves a dual role of increasing the renewable energy producer’s marginal operational profit and expanding the market demand for renewable electricity. When customers’ green consciousness is low, the government can only choose whether or not to implement the OS policy. Finally, we highlight that the implementation of the IS policy by the government may not be more beneficial to both the renewable energy producer and customers compared to the OS policy. This result informs regulators that energy security should be considered when designing subsidy policies and should not be limited to promoting the interests of participants.

Modeling and control system optimization for electrified vehicles: A data-driven approach

Learning-based intelligent energy management systems for plug-in hybrid electric vehicles (PHEVs) are crucial for achieving efficient energy utilization. However, their application faces system reliability challenges in the real world, which prevents widespread acceptance by original equipment manufacturers (OEMs). This paper begins by establishing a PHEV model based on physical and data-driven models, focusing on the high-fidelity training environment. It then proposes a real-vehicle application-oriented control framework, combining horizon-extended reinforcement learning (RL)-based energy management with the equivalent consumption minimization strategy (ECMS) to enhance practical applicability, and improves the flawed method of equivalent factor evaluation based on instantaneous driving cycle and powertrain states found in existing research. Finally, comprehensive simulation and hardware-in-the-loop validation are carried out which demonstrates the advantages of the proposed control framework in fuel economy over adaptive-ECMS and rule-based strategies. Compared to conventional RL architectures that directly control powertrain components, the proposed control method not only achieves similar optimality but also significantly enhances the disturbance resistance of the energy management system, providing an effective control framework for RL-based energy management strategies aimed at real-vehicle applications by OEMs.

Quantification of Corrosion on Cu Wire Bonding on Ag-Plated Lead Frame in HCl for Automotive Electronic Application

Copper (Cu) wire bonding has been widely used in automotive electronics as an interconnection technology. However, Cu wire bonding faces long-term reliability challenges due to corrosion, particularly when exposed to harsh environments. While the corrosion behavior and mechanisms of bulk-sized Cu metal are well studied, the corrosion behavior of submicron-sized Cu wire bonding in miniaturized components remains unclear. This study investigates the corrosion susceptibility of Cu wire with a 1 mil diameter, bonded to an Ag-plated leadframe in a small-outline transistor (SOT-23) package, commonly used in automotive electronics. The Cu wire bonds were subjected to varying concentrations of hydrochloric acid (HCl), specifically 3 M, 5 M, and 7 M, for exposure durations of 6, 12, and 18 hours. The samples were characterized using a field emission scanning electron microscope to observe the microstructure, and ImageJ software was used to quantify the corrosion of the Cu wire bonds. The findings show that the Cu wire bonding is susceptible to corrosion when exposed to high concentrations of HCl. The corrosion of the microstructure became more severe with increasing HCl concentration and exposure time. The affected area of the Cu wire bonding was 21% after 6 hours of exposure to 3 M HCl, increasing to 100% after 18 hours of exposure to 7 M HCl. This study reveals that the corrosion behavior of Cu wire bonding is a combination of selective and pitting-like corrosion, resulting in localized surface degradation rather than uniform corrosion. These findings provide valuable insights into the corrosion behavior of 1 mil Cu wire bonding in SOT-23 packages under highly corrosive environments.

Assessing Team Performance in a Longitudinal Neonatal Resuscitation Simulation Training Program: Comparing Validity Evidence to Select the Best Tool.

Introduction Neonatal resuscitation is a high-acuity, low-occurrence event that requires ongoing practice by interprofessional teams to maintain proficiency.Simulation provides an ideal platform for team training and evaluation of team performance. Our simulation center supports a longitudinal in situ simulation training program for delivery room teams. In addition to adherence to the Neonatal Resuscitation Program standards, team performance assessment is an essential component of program evaluation and participant feedback. Multiple published teamwork assessment tools exist. Our objective was to select the tool with the best validity evidence for our program's needs. Methods We used Messick's framework to assess the validity of evidence for potential teamwork assessment tools. Four possible tools were identified from the literature: the Mayo High Performance Teamwork Scale (Mayo), Team Performance Observation Tool (TPOT), Clinical Teamwork Scale (CTS), and Team Emergency Assessment Measure (TEAM). Relevant context included team versus individual focus, external evaluator versus self-evaluation, and ease of use (which included efficiency, clarity of interpretation, and overall assessment). Three simulation experts identified consensus anchors for each tool and independently reviewed and scored 10 pre-recorded neonatal resuscitation simulations. Raters assigned each tool a rating according to efficiency, ease of interpretation, and completeness of teamwork assessment. Interrater reliability (IRR) was calculated using intraclass correlation for each tool across the three raters. Average team performance scores for each tool were correlated with neonatal resuscitation adherence scores for each video using Spearman's rank coefficient. Results There was a range of IRR between the tools, with Mayo having the best (single 0.55 and multi 0.78). Each of the three raters ranked Mayo optimally in terms of efficiency (mean 4.66 + 0.577) and ease of use (4+1). However, TPOT and CTS scored highest (mean 4.66 ± 0.577) for overall completeness of teamwork assessment. There was no significant correlation to NRP adherence scores for any teamwork tool. Conclusion Of the four tools assessed, Mayo demonstrated moderate IRR and scored highest for its ease of use and efficiency, though not completeness of assessment. The remaining three tools had poor IRR, which is not an uncommon problem with teamwork assessment tools. Our process emphasizes the fact that assessment tool validity is contextual. Factors such as a relatively narrow (and high) performance distribution and clinical context may have contributed to reliability challenges for tools that offered a more complete teamwork assessment.

Enhancing safety and reliability in multistory construction: A multi-state system assessment of shoring/reshoring operations using interval-valued belief functions

Ensuring safety and reliability in multistory concrete construction necessitates rigorous assessment methodologies capable of addressing the inherent uncertainties of the construction process. This paper introduces a novel reliability assessment methodology for shoring/reshoring (S/R) operations, framed within a multi-state parallel-series system (MSS). Our approach innovatively combines concrete slab capacities, forming system strengths, and construction quality, integrating cost and time efficiencies. Central to our method is the use of Interval Universal Generating Functions (IUGF), which leverage Dempster-Shafer (D-S) theory and interval analysis to robustly estimate system reliability under uncertainty. For instance, the model considers concrete strength ranging from 0.72 × 2.1D to 0.96 × 2.1D at different curing stages, producing a system reliability index between 0.63 and 0.76, against a desired threshold of 0.8. This methodology produces interval-valued belief functions that capture a comprehensive range of potential system states and their likelihoods, offering a refined perspective on system safety. An illustrative case study demonstrates the practical application of this methodology in optimizing formwork removal and S/R schedules, significantly enhancing decision-making in construction operations. This paper aligns with the core themes of reliability engineering and system safety by addressing critical safety and reliability issues in complex technological systems through advanced probabilistic safety assessment methods. It significantly enriches the professional discourse by presenting a robust method that can be adapted across various engineering fields facing similar reliability challenges.