Search Results (30)

Efficient operation of interconnected production systems requires accurate modeling of upstream–downstream interactions to support informed operational decision-making. This study proposes an interconnected Adaptive Neuro-Fuzzy Inference System (ANFIS) framework consisting of two sequentially linked ANFIS models representing the upstream manufacturing stage and the downstream packaging stage, where the output of the first model is incorporated as an input to the second model. The framework uses operational Key Performance Indicators (KPIs), including mean time between failures (MTBF), mean time to repair (MTTR), uptime, reject rate, short stops, and long stops, to capture the nonlinear relationships between manufacturing and packaging operations. Unlike conventional single-model approaches, the proposed methodology represents the manufacturing and packaging stages as interconnected neuro-fuzzy subsystems, explicitly modeling their operational dependencies while maintaining model interpretability. Following data preprocessing and conditioning, two ANFIS models were developed using real industrial data and integrated into a MATLAB/Simulink environment for scenario-based analysis. The developed ANFIS models achieved low testing errors and satisfactory predictive performance on real industrial data. Simulation-based analyses were subsequently conducted to evaluate the effects of stoppage behavior and maintenance-related improvements on production-line uptime. The results indicate that the proposed framework captures upstream–downstream production dependencies and provides an interpretable decision-support tool for evaluating operational improvement scenarios in manufacturing environments. Full article

This study presents a five-state functional model for assessing the reliability of electric vehicle charging infrastructure from the perspective of charging-service capability. The model distinguishes full functionality, three levels of degradation, and the loss of the basic charging function. State changes are described using a time-homogeneous continuous-time Markov process that includes progressive degradation, sudden functional loss, and restoration to state S0. On this basis, MTTF, MTTR, and MTBF are reinterpreted in relation to first attainment, restoration trajectories, and complete functional cycles. The reference calculations determine stationary state probabilities, annual residence times, and the effectiveness of restoration before reaching S4. The results show that a low stationary probability of S4 may still correspond to a meaningful annual period of service unavailability, while most functional cycles end with restoration from intermediate states. The proposed framework provides a reproducible basis for analysing EV charging infrastructure and can be applied using transition intensities estimated from operator records. Full article

The formation of asphalt–resin–paraffin deposits (ARPDs) and gas hydrate deposits (GHDs) in oil wells equipped with electric submersible pumps (ESPs) remains a significant challenge in the oil and gas industry. This study aims to develop an inhibitory technology to prevent these deposits by utilizing the synergistic effect of chemical reagents combined with the optimization of ESP operating parameters. Based on previously published mathematical modeling and laboratory studies by the authors, this work presents the technological implementation of the inhibition system and its economic assessment. Specifically, optimal reagent dosages were calculated considering their synergistic interactions, a periodic injection regime was established, and the impact on the well’s mean time between failures (MTBF) was evaluated. Results demonstrate that optimizing ESP parameters shifts the onset depth of GHD formation by 119.4 m (25%) and ARPD formation by 72.6 m (6%). The application of the selected ARPD inhibitor at 0.055 wt.% reduced the thermodynamic hydrate inhibitor (methanol) dosage by 12.17% and enabled a transition from continuous to periodic methanol injection. Consequently, the predicted MTBF increased from 277 to 419 days (+50%). An eight-year economic analysis showed a positive net present value with a payback period of 14 months. Thus, the proposed technology is recommended for field testing in high-paraffin, low-resin oil fields operating under permafrost conditions. Full article

Reliability assessment of EMU onboard power supply boards is constrained by scarce field failure data and the intrusive nature of component-level degradation monitoring. Moreover, conventional linear degradation models may inadequately characterize the non-monotonic fluctuations and time-varying degradation rates of board-level health indicators. To address these limitations, this study proposes a mechanism-informed reliability assessment method using output ripple voltage as a non-invasive degradation indicator. A thermally accelerated degradation test was conducted on power supply boards used in video-monitoring servers, and a stable-baseline output-ripple relative-increment indicator was constructed to mitigate the effects of the initial burn-in process and sample-to-sample baseline differences. A nonlinear Wiener process with a power-law time scale was then developed to characterize the non-monotonic evolution, stochastic fluctuations, and time-varying degradation rate of the output ripple. Arrhenius-based lifetime extrapolation and time-censored MTBF analysis were subsequently performed. Compared with the linear and quadratic-drift Wiener processes, the proposed model achieved the largest maximized log-likelihood and the lowest AIC and BIC values; both information criteria were 22.56 lower than the corresponding values of the quadratic-drift model. Using an activation energy of 0.7 eV, the thermal acceleration factor was 41.19. Under the baseline scenario threshold of 150 mV, the predicted MTTF, R₉₀ lifetime, and R₅₀ lifetime were 16.20, 7.77, and 13.92 equivalent operating years, respectively, while the point estimate of the functional-failure-based time-censored MTBF was 43.45 years. The proposed method provides a non-invasive reliability assessment framework for condition monitoring, early warning, and preventive maintenance of EMU onboard power supply boards when failure data are scarce. Full article

The reliability of traction systems in urban rail transit vehicles is critical to safe and efficient operations. However, existing reliability assessment methods face challenges such as state space explosion, computational complexity, and difficulties in modeling fault interdependencies and imperfect maintenance. This paper proposes an integrated framework that combines dynamic fault trees with fault dependency models and Monte Carlo simulation. The method captures fault dependencies through functional dependency gates incorporating fault impact factors, models component degradation using a two-parameter Weibull distribution, and accounts for imperfect maintenance through a age reduction factor. The simulation efficiency of three random number generators—LCG, MT, and PCG64—was compared. A Monte Carlo simulation of 100,000 trials under maintenance-free conditions yielded a system MTBF of 29.9 months. A mode significance analysis identified the pantograph control unit and the traction control unit as the most critical components. Under targeted maintenance based on component criticality, the MTBF increased to 39.77 months—a 33% improvement—while the peak failure rate was maintained at approximately 3%. The MT generator exhibited the fastest convergence, achieving stability within 1% after 3000 iterations. This framework provides a practical foundation for optimizing preventive maintenance strategies for urban rail transit systems, and sensitivity analysis confirmed the robustness of weak link identification to parameter variations. This method is applicable to other complex systems with interdependent failures and multiple maintenance schedules. Full article

In Chinese solar greenhouses, row spacing of 40–80 cm and persistent 80–95% RH impose two fundamental constraints: conventional wheeled platforms exceed the inter-row turning radius, and unprotected electronics show <200 h MTBF under continuous humidity. These constraints define the design envelope for integrated inspection–detection–spraying robots. This review synthesizes 139 studies (2020–2026) from China, Europe, and North America. Beyond narrative synthesis, we contribute two quantitative tools: (1) a latency budget analysis decomposing the perception-to-spray pipeline—perception (30–50 ms), inference (50–140 ms), decision (5–10 ms), actuation (10–20 ms), and nozzle response (5–15 ms)—revealing a 100–235 ms total delay, translating to 3.0–7.1 cm spray-target displacement at 0.3 m/s; and (2) a standardized validation protocol with eight metrics for multi-site field trials. Four technical gaps persist. Rail-mounted platforms dominate, but autonomous systems lack validated high-humidity reliability. YOLO-based models achieve 93–94% mAP at 10–20 FPS on edge devices, yet cross-crop generalization is unproven, while Transformers are too slow (2–5 FPS) for real-time deployment. PWM-controlled spraying saves 20–40% of pesticide in controlled trials, but drift control and deposition uniformity under real canopies are rarely quantified with sufficient engineering detail. The quantified latency causes systematic spray-target misalignment that open-loop controllers cannot correct. We propose five future directions with quantifiable targets: low-cost modular platforms (50,000–80,000 RMB/unit vs. current 180,000–250,000 RMB); edge-optimized perception (>15 FPS on <15 W hardware); closed-loop latency compensation (<2 cm displacement); adoption of the proposed protocol for cross-system comparison; and multi-robot collaboration for >1 ha greenhouses. Full article

Traditional reliability models for distribution grids often rely on static historical averages, overestimating the operational lifespan of power system assets by neglecting the dynamic interplay between electrical loading and microclimatic stressors. This paper addresses these limitations by introducing an extended analytical framework designed to integrate climate-driven stressors into traditional reliability assessments, capturing the synergistic effects of environmental forcing and asset aging. This methodology is operationalized through a novel simulation framework and a modular Python-based tool (Python version 3.10.20), integrating OpenDSS and Pandapower to perform high-fidelity reliability assessments. By calculating instantaneous failure rates and Mean Time Between Failures (MTBF) as functions of real-time environmental forcing—specifically temperature and humidity-induced stresses—the proposed system captures degradation dynamics that remain invisible to conventional models. The framework’s capabilities are demonstrated through a simulation on a rural distribution grid, which explicitly includes auxiliary digitalization components, such as Remote Terminal Units (RTUs), that are frequently overlooked in standard benchmarks. The results reveal that environmental forcing triggers a sharp contraction in the MTBF of critical active assets, proving that asset seniority alone is an insufficient proxy for grid vulnerability. Full article

This study presents a multi-period Generalized Cell Formation Problem with Machine Availability (GCFP-MA) aimed at designing manufacturing cells that explicitly account for equipment reliability, maintainability, and temporal degradation. The proposed model extends classical formulations by introducing (i) availability-based constraints derived from Mean Time Between Failures (MTBF) and Mean Time to Repair (MTTR) and Markov-Chain models, (ii) downtime penalty costs reflecting non-production losses, and (iii) a multi-period horizon that captures system dynamics over time. To solve the resulting NP-hard problem, the Black Widow Optimizer (BWO)—a population-based metaheuristic inspired by cannibalistic reproduction—is implemented and validated against an exhaustive search benchmark. Computational experiments confirm that the BWO attains the global optimum with substantially reduced computational effort, achieving a balanced trade-off between exploration and exploitation. Results highlight that incorporating availability and repair dynamics prevents infeasible or over-optimistic configurations and yields cost-effective, robust cell layouts. The proposed approach provides both theoretical and practical contributions by integrating availability engineering and production system design within a unified optimization framework. Full article

Designing cost-effective, reliable diagnostic sensor suites for complex assets remains challenging due to conflicting objectives across stakeholders. A holistic framework that integrates the Normalised Diagnostic Contribution Index (NDCI)—which scores sensors by separation power, severity sensitivity, and uniqueness—with a Multi-Objective Sensor Optimisation Framework (MOSOF) is presented. Using a high-fidelity virtual aircraft model coupling engine, fuel, electrical power system (EPS), and environmental control system (ECS), NDCI against minimum Redundancy-maximum Relevance (mRMR) is benchmarked under a rigorous nested cross-validation protocol. Across subsystems, NDCI yields more compact suites and higher diagnostic accuracy, notably for engine (88.6% vs. 69.0%) and ECS (67.7% vs. 52.0%). Then, a multi-objective optimisation reflecting an airline use-case (diagnostic performance, cost, reliability, and benefit-to-cost) is executed, identifying a practical Pareto-optimal ‘knee’ solution comprising 12–14 sensors. The recommended suite delivers a normalised performance of ≈0.69 at ≈USD36k with ≈145 kh MTBF, balancing the cross-subsystem information value with implementation constraints. The NDCI-MOSOF workflow provides a transparent, reproducible pathway from raw multi-sensor data to stakeholder-aware design decisions, and constitutes transferable evidence for model-based safety and certification processes in Integrated Vehicle Health Management (IVHM). The limitations (simulation bias, cost/MTBF estimates), validation on rigs or in-service fleets, and extensions to prognostics objectives are discussed. Full article

Given the pressing need to strengthen operational reliability in electrical distribution networks, this study proposes an optimization methodology based on the Teaching–Learning-Based Optimization (TLBO) algorithm for the strategic location of redundant lines. The model is validated on the “MV Distribution Network—Base Model” test system, considering the combination of the MTBF (Mean Time Between Failures) and MTTR (Mean Time To Repair) indicators as the objective function. After 500 independent runs, it is determined that the configuration with three redundant lines identified as LN_1011, LN_1058, and LN_0871 offers the most stable solution. Specifically, this topology increases the MTBF from 403.64 h to 409.42 h and reduces the MTTR from 2.351 h to 2.306 h. In addition, significant improvements are observed in the voltage profile and angle, along with a more balanced redistribution of active and reactive power, more efficient use of existing lines, and an overall reduction in energy losses. Full article

Accurate fault risk assessment is essential for maintaining wind turbine reliability. Traditional failure modes and effects analysis (FMEA)-based approaches struggle to handle the fuzziness, uncertainty, and conflicting nature of multi-criteria evaluations, which may lead to delayed fault detection and increased maintenance risks. To address these limitations, this paper proposes an enhanced risk assessment framework that integrates subjective-objective weighting and fuzzy decision-making. First, a combined subjective–objective weighting (CSOW) model with adaptive fusion is developed by integrating the analytic hierarchy process (AHP) and the entropy weight method (EWM). The CSOW model optimizes the weighting of severity (S), occurrence (O), and detection (D) indicators by balancing expert knowledge and data-driven information. Second, a fuzzy decision-making model based on interval-valued intuitionistic fuzzy numbers and VIKOR (IVIFN-VIKOR) is established to represent expert evaluations and determine risk rankings. Notably, the overlap rate between the top 10 failure modes identified by the proposed method and a fault-tree-based Monte Carlo simulation incorporating mean time between failures (MTBF) and mean time to repair (MTTR) reaches 90%, substantially higher than other methods. This confirms the superior performance of the framework and provides enterprises with a systematic approach for risk assessment and maintenance planning. Full article

Predictive maintenance (PdM) of biomedical equipment is increasingly recognized as a strategic lever to enhance reliability and ensure continuity of care. Yet, in resource-limited hospitals, implementation is hindered by fragmented data sources, non-standardized codification, and weak interoperability. Few studies have demonstrated the feasibility of structuring PdM data from real hospital interventions in middle-income countries. This work presents a prototype data structuring pipeline applied to six public hospitals in the Casablanca–Settat region of Morocco. The pipeline consolidates 6816 validated maintenance interventions from 780 devices across 30 departments and integrates normalized reliability indicators (Failure Rate, MTBF, MTTR corrected with IQR, and Downtime Hours). It ensures semantic harmonization, auditability, and reproducibility, resulting in a structured and interoperable dataset that constitutes a regional first in the Moroccan hospital context. To illustrate predictive potential, a proof-of-concept Random Forest model was evaluated. It achieved AUROC = 0.65 on the full imbalanced dataset and AUROC = 0.82 on a balanced 2000-intervention subset, confirming the dataset’s discriminative value while reflecting real-world challenges. This work bridges the gap between conceptual PdM frameworks and operational hospital realities, and establishes a replicable foundation for AI-driven predictive maintenance in low-resource healthcare environments. Full article

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

The reliability of unmanned aerial vehicle (UAV) energy storage battery systems is critical for ensuring their safe operation and efficient mission execution, and has the potential to significantly advance applications in logistics, monitoring, and emergency response. This paper reviews theoretical and technical advancements in UAV battery reliability, covering definitions and metrics, modeling approaches, state estimation, fault diagnosis, and battery management system (BMS) technologies. Based on international standards, reliability encompasses performance stability, environmental adaptability, and safety redundancy, encompassing metrics such as the capacity retention rate, mean time between failures (MTBF), and thermal runaway warning time. Modeling methods for reliability include mathematical, data-driven, and hybrid models, which are evaluated for accuracy and efficiency under dynamic conditions. State estimation focuses on five key battery parameters and compares neural network, regression, and optimization algorithms in complex flight scenarios. Fault diagnosis involves feature extraction, time-series modeling, and probabilistic inference, with multimodal fusion strategies being proposed for faults like overcharge and thermal runaway. BMS technologies include state monitoring, protection, and optimization, and balancing strategies and the potential of intelligent algorithms are being explored. Challenges in this field include non-unified standards, limited model generalization, and complexity in diagnosing concurrent faults. Future research should prioritize multi-physics-coupled modeling, AI-driven predictive techniques, and cybersecurity to enhance the reliability and intelligence of battery systems in order to support the sustainable development of unmanned systems. Full article

Network exposure surface analysis aims to identify network assets that are exposed to the Internet and is critical for enterprise security. However, existing tools face two key challenges: combinatorial explosion in traditional packet testing, and high false positive rates in firewall-based static analysis. To address these issues, this paper proposes a network model-based approach to accurately characterize the forwarding behaviors of devices in enterprise networks, and performs network-level static analysis on the established graph model. Specifically, we construct a device-level forwarding graph using detailed element models for switches and firewalls, capturing the semantics of the forwarding information base, virtual routing and forwarding, virtual systems, and security zones. We further introduce a parallelized multi-threaded breadth-first search (MTBFS) algorithm to efficiently identify reachable assets from Internet-facing ingress interfaces. Experimental results demonstrate a 20× speedup over traditional methods in a large-scale enterprise network consisting of 7970 switches and 16 Internet-facing interfaces. Full article