Search Results (193)

Negative Ion Source Neutral beam Injection (NNBI), as a critical auxiliary heating system for magnetic confinement fusion devices, directly affects the plasma heating efficiency of tokamak devices through the reliability of its beam source system. The single-shot experiment constitutes a significant experimental program for NNBI. This study addresses the frequent equipment failures encountered by the NNBI beam source system during a cycle of experiments, employing fault tree analysis (FTA) to conduct a systematic reliability assessment. Utilizing the AutoFTA 3.9 software platform, a fault tree model of the beam source system was established. Minimal cut set analysis was performed to identify the system’s weak points. The research employed AutoFTA 3.9 for both qualitative analysis and quantitative calculations, obtaining the failure probabilities of critical components. Furthermore, the F-V importance measure and mean time between failures (MTBF) were applied to analyze the system. This provides a theoretical basis and practical engineering guidance for enhancing the operational reliability of the NNBI system. The evaluation methodology developed in this study can be extended and applied to the reliability analysis of other high-power particle acceleration systems. Full article

Common-mode analysis (CMA) is a qualitative analytical method used to support the evaluation of independence in the system safety assessment of civil aircraft. In traditional CMA, independence requirements are usually identified by evaluating the combination of events using the fault tree AND-gates. This approach is cumbersome and highly dependent on the skills and experiences of system safety engineers. An Architecture Analysis and Design Language (AADL)-based methodology is proposed to derive independence requirements for CMA. Error propagation data in AADL is extracted to develop a fault propagation model. Subsequently, potential factors contributing to common-mode failures (CMFs) are identified using the fault propagation model. A Primary Flight Computer (PFC) of an aircraft is used as a case study to illustrate the effectiveness of our proposed method. Full article

Ocean-going vessels rely on marine diesel engines, referred to as the main engine, to carry the vessel’s load and ensure safe travel. These engines play a critical role, as their operation impacts on all aspects of the vessel’s functionality. To meet increasing demands for extended run times while maintaining reliability, it is essential to address the risks of main engine failure. Previous studies have highlighted numerous accidents resulting from such failures. Consequently, the reliability of the main propulsion engine is a crucial component of safe vessel operation. This study addresses the lack of methodologies for predicting engine reliability using failure running hours (FRHs). A data-driven model was developed using FRH data collected from marine engineers during on-board maintenance operations. Additionally, fault tree analysis (FTA) was employed to calculate the reliability of individual subsystems and the overall main propulsion engine. The findings indicate that the lube oil system, freshwater cooling system, scavenge system, and fuel system reach 0% reliability at approximately 2000 h, 14,000 h, 2500 h, and 1400 h of operation, respectively. Additionally, the reliability of the main propulsion engine drops to 0% after around 900 h of operation. By incorporating this prediction model, ship operators can better schedule maintenance, significantly enhancing engine reliability and reducing maritime accidents. This approach contributes to safer and more efficient operations for commercial marine systems. This study represents a vital step toward improving the reliability of ocean-going vessels. Full article

This research explores Dynamic Probabilistic Risk Assessment (DPRA) using EMRALD to evaluate the reliability and safety of passive safety systems in nuclear reactors, with a focus on mitigating Loss of Coolant Accidents (LOCAs). The BWRX-300 Small Modular Reactor (SMR) is used as an example to illustrate the proposed DPRA methodology, which is broadly applicable for enhancing traditional Probabilistic Safety Assessment (PSA). Unlike static PSA, DPRA incorporates time-dependent interactions and system dynamics, allowing for a more realistic assessment of accident progression. EMRALD enables the modelling of system failures and interactions in real time using dynamic event trees and Monte Carlo simulations. This study identifies critical vulnerabilities in passive safety systems and quantifies the Core Damage Frequency (CDF) under LOCA scenarios. The findings demonstrate the advantages of DPRA over traditional PSA in capturing complex failure mechanisms and providing a more comprehensive and accurate risk assessment. The insights gained from this research contribute to improving passive safety system designs and enhancing nuclear reactor safety strategies for next-generation reactors. Full article

The air transportation system is composed of multiple elements and belongs to a complex socio-technical system. It is difficult to assess the risk of an aircraft fault because it could constantly change during operation and is influenced by numerous factors. Although traditional methods such as Failure Mode, Effects, and Criticality Analysis (FMECA) and Fault Tree Analysis (FTA) can reflect the degree of fault risk to a certain extent, they cannot accurately quantify and evaluate the fault risk under the multiple influences of human factors, random faults, and external environment. In order to solve these problems, this article proposes a fault risk assessment method for aircraft control systems based on a fault risk composite assessment framework using the Improved Risk Priority Number (IRPN) as the basis for the fault risk assessment. Firstly, a Bayesian network (BN) and Gated Recurrent Unit (GRU) are introduced into the traditional evaluation framework, and a hybrid prediction model combining static and dynamic failure probability is constructed. Subsequently, this paper uses the functional resonance analysis method (FRAM) by introducing a risk damping coefficient to analyze the propagation and evolution of fault risks and accurately evaluate the coupling effects between different functional modules in the system. Finally, taking the fault of a jammed flap/slat drive mechanism as an example, the risk of the fault is evaluated by calculating the IRPN. The calculation results show that the comprehensive failure probability of the aircraft control system in this case is 3.503 × 10⁻⁴. Taking into account the severity, the detection, and the risk damping coefficient, the calculation result of IRPN is 158.00. According to the classification standard of the risk level, the failure risk level of the aircraft belongs to a controlled risk, and emergency measures need to be taken, which is consistent with the actual disposal decision in this case. Therefore, the evaluation framework proposed in this article not only supports a quantitative assessment of system safety and provides a new method for fault risk assessments in aviation safety management but also provides a theoretical basis and practical guidance for optimizing fault response strategies. Full article

Trees play a vital role in urban environments by mitigating heat islands, floods, and pollution, while promoting public health and well-being. Acoustic tomography is an effective tool for assessing tree integrity, but its high-cost limits widespread use. To reduce costs, this study evaluated the use of ultrasonic tomography with standardized velocity thresholds (VTs) for detecting cavities and decay in trunks. A total of 38 discs from 21 trees species were analyzed using different VTs (35%, 40%, 45%, and 50%). The results showed that thresholds of 35% Vmax for cavity detection and 50% Vmax for cavity with decay detection can be adopted for tomographic image assessments of trees, regardless of species. Using the same velocity thresholds regardless of species enables the practical application of the technology, with average accuracy losses (below 5%) that are quite reasonable considering the variability of the material under inspection. These findings support the broader use of technology in tree failure risk assessments. Full article

Tree root intrusion can cause failures of underground sewer pipes and thus represent a major water asset management issue. If tree root intrusion is not detected early, this may lead to the interruption of wastewater services and high costs of repair to the pipeline. The objectives of this review are to assess the existing maintenance strategies, explore suitable strategies for Australia and similar settings around the world, and identify possible factors and predictive tools. Maintenance strategies can be divided into two categories: reactive and proactive approaches. The current reactive approaches are (1) mechanical techniques to clean the root mass in pipe networks and (2) chemical techniques to remove the root mass and control future growth. The literature suggests that the reactive approaches often provide only partial solutions. The proactive approaches, guided by a predictive model of tree root intrusion and its related factors, showed the potential to improve maintenance and limit the risk of the damage from re-occurring. Predictive models could help to evaluate the risk of planting trees in different conditions and minimise the damage of tree root intrusion after further multifactor investigations. Full article

Multimodal freight transport systems, integrating maritime, rail, and road modes, play a vital role in modern logistics but face elevated operational, human, and environmental risks due to their complexity and interdependencies. To address the limitations of conventional risk assessment methods, this study proposes a hybrid risk modeling framework that integrates fault tree analysis (FTA), dynamic fault trees (DFTs), and fuzzy logic reasoning. This approach supports the modeling of sequential failures and captures qualitative uncertainties such as human fatigue and inadequate training. The framework incorporates reliability metrics, including Mean Time to Failure (MTTF) and Mean Time Between Failures (MTBF), enabling the quantification of system resilience and identification of critical failure pathways. Application of the model revealed human error, particularly procedural violations, insufficient training, and fatigue, as the dominant risk factor across transport modes. Road transport exhibited the highest probability of risk occurrence (p = 0.9960), followed by rail (p = 0.9937) and maritime (p = 0.9900). By integrating probabilistic reasoning with qualitative insights, the proposed model offers a flexible decision support tool for logistics operators and policymakers, enabling scenario-based risk planning and enhancing system robustness under uncertainty. Full article

The early diagnosis of dementia, a progressive condition impairing memory, cognition, and functional ability in older adults, is essential for timely intervention and improved patient outcomes. This study proposes a novel multiclass classification that differentiates dementia from other comorbid conditions, specifically cardiovascular diseases, including heart failure and aortic valve disorder, by leveraging the “blessing of dimensionality” to enhance predictive performance while ensuring feature accessibility. Using a dataset of 26,474 electronic health records from two hospitals in Chiang Rai, Thailand, the proposed framework introduced clinically informed feature augmentation to enhance model generalizability. Furthermore, the borderline synthetic minority oversampling technique was employed to address class imbalance, enhancing the model’s performance for minority classes. This study systematically evaluated a suite of machine learning models, including extreme gradient boosting, gradient boosting, random forest, support vector machine, decision trees, k-nearest neighbors, extra trees, and TabNet, across both the original and enriched datasets, with the latter integrating augmented features and synthetic data. Predictive performance was assessed using accuracy, precision, recall, F1 score, area under the receiver operating characteristic curve, and area under the precision–recall curve. The results revealed that all the models exhibited consistent performance improvements with the enriched dataset, affirming the value of dimensionality when guided by domain expertise. Full article

The maritime transport of liquefied gases poses significant safety and environmental hazards such as fire, explosion, toxic gas emissions, and air pollution. The main objective of this study was to systematically identify, analyze, and prioritise the potential risks associated with the operation of liquefied gas tankers using a hybrid methodological framework. This framework integrates Fuzzy Delphi, Fuzzy DEMATEL, and Fault Tree Analysis (FTA) techniques to provide a comprehensive risk assessment. Initially, 20 key risk factors were identified through expert consensus using the Fuzzy Delphi method. The causal relationships between these factors were then assessed using Fuzzy DEMATEL to understand their interdependencies. Based on these results, accident probabilities were further analyzed using FTA modelling. The results show that fires, explosions, and large gas leaks are the most serious threats. Equipment failures—often caused by corrosion and operational errors by crew members—are also significant contributors. In contrast, cyber-related risks were found to be of lower criticality. The study highlights the need for improved crew training, rigorous inspection mechanisms, and the implementation of robust preventive risk controls. It also suggests that the prioritisation of these risks may need to be reevaluated as autonomous ship technologies become more widespread. By mapping the interrelated structure of operational hazards, this research contributes to a more integrated and strategic approach to risk management in the LNG/LPG shipping industry. Full article

Friction stir welding (FSW) is a solid-state joining technique widely used for aluminum alloys in aerospace, automotive, and shipbuilding applications, yet the prediction of fatigue fracture locations within FSW joints remains challenging for structural-life assessment. In this study, we investigate fatigue fracture location prediction in 7075-T651 aluminum alloy FSW joints by applying four machine learning methods—decision tree, logistic regression, a three-layer back-propagation artificial neural network (BP ANN), and a novel Quadratic Classification Neural Network (QCNN)—using maximum stress, stress amplitude, and stress ratio as input features. Evaluated on an experimental test set of eight loading conditions, the QCNN achieved the highest accuracy of 87.5%, outperforming BP ANN (75%), logistic regression (50%), and decision tree (37.5%). Building on QCNN outputs and incorporating relevant material property parameters, we derive a Regional Fracture Prediction Formula (RFPF) based on a Fourier-series quadratic expansion, enabling the rapid estimation of fracture zones under varying loads. These results demonstrate the QCNN’s superior predictive capability and the practical utility of the RFPF framework for the fatigue failure analysis and service-life assessment of FSW structures. Full article

The operational efficiency and reliability of the ship’s lubrication oil system directly impact the vessel’s safety. Traditional reliability analysis methods struggle to effectively handle the system’s dynamic characteristics and multi-source data analysis. To address these issues, this study proposes an innovative method that integrates feature dimensionality reduction, a dynamic Bayesian network of gravity model to improve the accuracy of system reliability analysis. First, the proportional hazards model is used to evaluate the operational reliability of each component, providing a quantitative basis for assessing the system’s health status through failure rate estimation. Then, a dynamic Bayesian network model is employed for overall system reliability analysis, fully considering the impact of multi-state devices and different maintenance strategies. The proposed DBN-based reliability assessment method achieves significant improvements over the traditional Fault Tree Analysis (FTA). The reliability of the main lubrication oil system (GUB) increases from 0.169 to 0.261, representing a 9.2% improvement; under scheduled maintenance conditions, the system reliability stabilizes at approximately 0.9873 after $0.4\times {10}^5$ h, compared to only 0.24 without maintenance. The proposed method effectively evaluates the reliability of the lubrication oil system, and the maintenance strategy using this method can greatly improve the reliability, providing strong support for scientifically guiding maintenance decisions. Full article

In operational testing contexts, testers face dual challenges of constrained timeframes and limited resources, both of which impede the generation of reliability test data. To address this issue, integrating data from similar systems with test data can effectively expand data sources. This study proposes a systematic approach wherein the mission of the system under test (SUT) is decomposed to identify candidate subsystems for data combination. A phylogenetic tree representation is constructed for subsystem analysis and subsequently mapped to a mixed-integer programming (MIP) model, enabling efficient computation of similarity factors. A reliability assessment model that combines data from similar subsystems is established. The similarity factor is regarded as a covariate, and the regression relationship between it and the subsystem failure-time distribution is established. The joint posterior distribution of regression coefficients is derived using Bayesian theory, which are then sampled via the No-U-Turn Sampler (NUTS) algorithm to obtain reliability estimates. Numerical case studies demonstrate that the proposed method outperforms existing approaches, yielding more robust similarity factors and higher accuracy in reliability assessments. Full article

The early detection of faults in photovoltaic (PV) systems is crucial for ensuring efficiency, minimizing energy losses, and extending operational lifespan. This study evaluates and compares multiple machine-learning models for fault diagnosis in PV systems, analyzing their performance across different fault types and operational modes. A dataset comprising 2.2 million measurements from a laboratory-based PV model, covering seven fault categories—including inverter failures, partial shading, and sensor faults—is used for training and validation. Models are assessed under both Maximum Power Point Tracking (MPPT) and Limited Power Point Tracking (LPPT) conditions to determine their adaptability. The results indicate that the ensemble bagged tree classifier achieves the highest accuracy (92.2%) across all fault scenarios, while neural network-based models perform better under MPPT conditions. Additionally, the study highlights variations in model performance based on power mode, suggesting the potential for adaptive diagnostic approaches. The findings reinforce the feasibility of machine learning for predictive maintenance in PV systems, offering a cost-effective, sensor-free method for real-time fault detection. Future research should explore hybrid models that dynamically switch between classifiers based on system conditions, as well as validation using real-world PV installations. Full article

The topic of hydrogen as an energy vector is widely discussed in the present literature, being one of the crucial technologies aimed at human carbon footprint reduction. There are different hydrogen production methods. In particular, this paper focuses on Steam Methane Reforming (SMR), which requires a source of high-temperature heat (around 900 °C) to trigger the chemical reaction between steam and CH₄. This paper examines a plant in which the reforming heat is supplied through a helium-cooled high-temperature nuclear reactor (HTR). After a review of the recent literature, this paper provides a description of the plant and its main components, with a central focus on the safety and reliability features of the combined nuclear and chemical system. The main aspect emphasized in this paper is the assessment of the hydrogen production reliability, carried out through Failure Modes and Effects Analysis (FMEA) with the aid of simulation software able to determine the quantity and origin of plant stops based on its operational tree. The analysis covers a time span of 20 years, and the results provide a breakdown of all the failures that occurred, together with proposals aimed at improving reliability. Full article