Component Failure Rates Research Articles

Evaluation of reliability and characteristics measure using exponential decay distribution of non‐series parallel network

AbstractThe purpose of this paper is to analyze the reliability and characteristics of the non‐series‐parallel network. The illustrative non‐series parallel networks, that is, the Twin T‐Network and the network with six components of resistor and capacitor, have been converted into a series of parallel network combinations by use of the minimal path technique. The stochastic variables related to component failure rate have been demonstrated using exponential decay distribution. We obtain the reliability, mean time to failure, and sensitivity with the use of the exponential decay distribution. The reliability and its attributes are examined by assuming different values of scale and shape parameters at different points of time. Using Owen's methodology, we obtained minimal signature, tail signature, expected time, expected cost, and Barlow‐Proschan index were determined. The performance of all reliability characteristics has been observed graphically for arbitrary values of parameters. A real‐world example is used to introduce the point.

Optimal preventive maintenance: Balancing reliability and costs in the electricity market

This study addresses the challenge of achieving optimal preventive maintenance within power systems, aiming to balance reliability and costs effectively. The primary focus is on understanding the relationship between preventive maintenance expenditures and the failure rate of essential transmission components, particularly transformers in substations. We contribute to the existing literature by providing two key insights. Firstly, we establish a theoretical framework for determining the optimal level of preventive maintenance, which can be extended across various electricity facilities. By analyzing the relationship between preventive maintenance costs and failure rates, our goal is to identify the investment level that ensures a reliable power system while minimizing financial burdens. Secondly, we explore the functional form that accurately characterizes the link between preventive maintenance costs and failure rates. The diminishing marginal rate of failure, dependent on different functional forms, highlights the variability of optimal preventive maintenance expenses. Utilizing Lasso regression, we identify the functional form that optimally characterizes the relationship between outage occurrences and maintenance costs. These insights extend well beyond the confines of the Korean power industry, offering a sustainable approach to managing transmission facilities and enhancing the overall stability and efficiency of global electricity networks.

A rolling horizon for rolling stock maintenance scheduling problem with cyclical activities

The rolling stock maintenance scheduling problem addressed here involves scheduling a set of cyclical preventive maintenance activities while considering resource capacity and geographically dispersed depots. The objective is to minimize both corrective and preventive maintenance costs using reliability features. We propose to use rolling horizon to decompose the problem into several successive subproblems such that each one tackles the scheduling of only the next cycle of activities instead of all cycles. The scheduled dates to be fixed should be as close as possible to their respective deadlines. Each train can have three status (in service, in maintenance, out of service). The activities deadlines are determined depending on component failure rate which has to be respected otherwise the train becomes out of service. An integer linear programming (ILP) model is proposed to solve the subproblem using a time oriented modeling to get an accurate maintenance cost (preventive and corrective) and respect a minimum of available trains. The rolling horizon allows to build a feasible schedule within a predefined planning horizon for moderate size problems with an interesting trade-off between computation time and solution quality.

An analytical method for reliability evaluation of power distribution system with time-varying failure rates

Reliability evaluation is crucial in assessing the power supply capacity and guiding the planning and operation of the distribution system. Traditional analytical reliability evaluation usually assumes a constant failure rate for components, implying that the life distribution follows an exponential distribution. However, in reality, the failure rate of components varies over time due to external disturbances and aging. As a result, traditional analytical reliability evaluation methods are no longer applicable. To address this issue, this paper presents an analytical method for evaluating the reliability of the distribution system with time-varying failure rates. The method considers short-term and long-term time-varying failure rates using density evolution and non-homogeneous Poisson process techniques, respectively. Both steady-state and instantaneous indices are calculated to reflect the level of system reliability. Finally, the proposed method is applied to the IEEE RBTS Bus 6 test system, demonstrating its correctness and efficiency.

O&M-aware techno-economic assessment for floating offshore wind farms: A geospatial evaluation off the North Sea and the Iberian Peninsula

The development of accurate techno-economic models is crucial to boost the commercialisation of floating offshore wind farms. However, conventional techno-economic models oversimplify operation and maintenance (O&M) aspects, neglecting key maintenance factors, such as component failure rates, metocean conditions, repair times, maintenance vessels and ports. To address this limitation, this paper presents an O&M-aware techno-economic model that comprehensively incorporates the most relevant maintenance factors and evaluates their impacts on site-identification across the North Sea and the Iberian Peninsula based on diverse O&M strategies. Results reveal that operational expenditure can contribute significantly to the levelised cost of energy, ranging from 22% to 50% in the North Sea and 19% to 46% in the Iberian Peninsula. Furthermore, results demonstrate that suitable sites vary based on O&M strategy: preventive strategies favour areas with abundant wind resources like northern Scotland, Norway and Galicia, whereas corrective strategy prioritise sites with less severe metocean conditions, such as southern Scotland and extensive regions in the Mediterranean Sea, including the Gulf of Roses and the Alboran Sea. Finally, the downtime of turbines, an aspect traditionally neglected in techno economic frameworks, emerges as a key factor for accurate techno-economic assessment and site-identification.

Performance assessment of a newly developed non-invasive 2D beam profile monitor for high-intensity accelerators

In high-intensity accelerators, minimizing beam loss is paramount to avoid damages and activation of accelerator components. To achieve this, a reliable beam profile monitor is essential for assessing the beam center and spread. Additionally, the beam profile can be used to steer the beam during regular operation, thus reducing beam loss. In cases of irregular events, the monitor can initiate beam profile recording to identify and address any issues promptly. In this manuscript, we present a detailed description of a non-invasive 2D beam profile monitor, focusing on its components, operation, and most importantly, its reliability and failure modes. From operation experience, failure modes of the system and its effects were identified. Fault tree analysis was carried out. Failure rate of sheet generator was estimated using response surface method. Failure rates of components were used to estimate the failure rate of the system. From the target reliability, the required maximum repair time of the system was calculated.

Fuelling the Future with Safe Hydrogen Transportation Through Natural Gas Pipelines: A Quantitative Risk Assessment Approach

AbstractThe global transition to clean and sustainable energy sources has sparked interest in hydrogen as a potential solution to reduce greenhouse gas emissions. Efficient and safe transportation of hydrogen is crucial for its integration into the energy network. One approach is utilizing existing natural gas infrastructure, but it introduces unique challenges. Hydrogen has distinct characteristics that pose potential hazards, requiring careful consideration for safe transportation through natural gas pipelines. Moreover, the absence of field data on component failure rates adds to the existing uncertainty in Quantitative Risk Assessment (QRA) for hydrogen transportation. QRA plays a vital role in enabling the safe deployment of hydrogen transportation through existing pipelines and is increasingly integrated into the permitting process. The lack of data impedes the comprehensive understanding of risks associated with hydrogen transportation. This paper aims not only to analyse the effects of hydrogen blending ratios on gas dispersion, release rates, jet fires, and explosions in natural gas pipelines, but also highlight the disparities in leak frequencies currently used for hydrogen or blended hydrogen. A QRA for hydrogen blending in natural gas pipelines is novel and timely because the behaviour of hydrogen in natural gas pipelines, a novel process with potential hazards, is not fully understood. Conducting a thorough QRA on hydrogen blending in gas pipelines, our study reveals innovative insights: higher blending ratios reduce release rates, impact safe distances, and maintain stable flame lengths. Despite an elevated explosion risk, scenarios remained below lethal overpressure values. This paper offers unique contributions to safety considerations in hydrogen transportation, guiding stakeholders toward informed decisions for a secure and sustainable energy future.

A component diagnostic and prognostic framework for pump bearings based on deep learning with data augmentation

To support the mission of providing safe electricity generation with a high capacity factor, a Predictive Maintenance (PdM) framework using Machine Learning Models (MLM) to optimize component maintenance operations is developed. Using sensor measurements to better predict the true component’s Remaining Useful Life (RUL), the PdM framework has the potential to optimize maintenance costs by performing maintenance only when necessary. The PdM framework to pump bearings, the framework consists of a Convolutional Neural Network Autoencoder (CNN-AE) to detect component deviations from normality, a CNN to characterize component fault modes, and a Bayesian Neural Network (BNN) to estimate the component RUL with uncertainty. To increase the number of training samples, a synthetic data generation procedure was developed and includes procedures to recreate the fault-specific characteristic frequencies for diagnostics and the degradation trends for prognostics. The MLMs trained on the synthetic data are tested on the Center for Intelligent Maintenance Systems (IMS) dataset to showcase how well the synthetic data replicates measurement data. Utilizing this framework, the PdM was found to delay maintenance on average by a total of 8.92 years over 40 years and decrease the unexpected component failure rate from 10% to 0% when compared to traditional maintenance philosophies.

Improved Survival Rates with Fixed Bearing Total Ankle Arthroplasty

Introduction/Purpose: With increasing total ankle arthroplasty (TAA) being performed and a plethora of new survivorship data available, an updated literature review is needed to better understand the impact of different types of implants on the outcomes of TAA. The purpose of this study is to compare the outcomes of fixed vs mobile bearing TAAs in the literature. Methods: A comprehensive search of MEDLINE for all articles published between 2004 and 2023 was conducted with a minimum two-year mean follow-up. Two reviewers evaluated each study to determine whether it was eligible for inclusion and abstracted the data of interest. Meta-analytic pooling of group results across studies was performed, examining implant survival and component failure. Seventy-three implant groups met inclusion criteria (only 3rd and 4th generation implants), composed of STAR (20), Salto (mobile bearing) (10), Salto Talaris (11), Hintegra (8), Zimmer (6), INBONE II (6), INBONE I (4), Infinity (4), Cadence (4). 41 groups (56.2%) were implanted with mobile bearing devices, and 32 (43.8%) with fixed bearing implants. In total, 6498 subjects were included, with a mean age of 61.77 years and a mean BMI of 28.5 kg/m2. At a mean follow-up of 62.2 months, the overall reoperation rate was 21.13% and the metal component revision rate was 10.16%. Results: The mean follow-up of mobile bearing TAA studies (N =41) was 77.8 (range 24-188.4) months, and that of fixed bearing studies (N =32) was 45.3 (range 24-85.2) months. Multivariate analysis of bearing type, controlling for follow-up duration, found fixed bearing implants had statistically significantly higher metal component survival rates (fixed=96.1%, mobile=87.9%, p=.001); however, there were no statistically significant differences in rates of reoperation (fixed=17.0%, mobile=24.8%, p=.768). When examining specifically tibial and talar component failure rates, there was no statistically significant difference between bearing type (fixed tibial=1.46%, mobile tibial=2.95%, p=.472) (fixed talar=2.19%, mobile talar=2.79%, p=.966). Overall implant survival rates at one, two, five, and ten years were 96.4%, 96.0%, 92.7%, and 79.4%, respectively, and did not differ by bearing type (Table 1). Conclusion: Implant survival was statistically significantly better for 3rd and 4th generation fixed bearing TAAs as compared to mobile bearing TAAs, controlling for length of follow-up. There was no statistically significant difference in total reoperation rate. This suggests that fixed bearing implants may offer improved implant survival compared to mobile bearing implants, though further research is needed to confirm these findings.

An intelligent schedule maintenance method for hydrogen fuel cell vehicles based on deep reinforcement learning considering dynamic reliability

Schedule maintenance activities for hydrogen fuel cell vehicles are aimed to keep them in better condition in long-term operation. In this paper, we develop a reinforcement learning based schedule maintenance strategy, which is used to comprehensively consider the balance between safety and maintenance cost, so as to find the optimal maintenance strategy. A multi-level framework is established with remaining useful life of key components, fault tree analysis, and logistics cost model to generate an exploration environment that can express the operational stability, the failure rate of components, and the cost of repair and storage. The trained agent combines a deep neural network to explore the optimal strategy under the dynamic reliability of key components. Finite steps are the constraints. Safety rates, maintenance cost, and episode of operation are incorporated into a multi-objective reward function. The hydrogen supply circuit of fuel cell vehicle is simulated via Python. The trained agent is compared with traditional time-based schedule maintenance strategy and corrective maintenance strategy. The results show that the reinforcement learning based schedule maintenance agent has optimized the total reward, cost control and accident rate by 77%, 59% and 5%, respectively, compared with the traditional time-based schedule maintenance strategy. In the safe operation management of fuel cell vehicles, efficient and stable decision-making ability has been achieved.

Analysis of safe and effective next-generation rail signalling systems

Moving Block (MB) and Virtual Coupling (VC) rail signalling will change current train operation paradigm by migrating vital equipment from trackside to onboard to reduce train separation and maintenance costs. Their actual deployment is however constrained by the industry’s need to identify configurations of MB and VC signalling equipment which can effectively guarantee safe train movements even under degraded operational conditions involving component faults. In this paper, we analyse the effectivity of MB and VC in safely supervising train separation under nominal and degraded conditions by using an innovative approach which combines Fault Tree Analysis (FTA) and Stochastic Activity Networks (SAN). An FTA model of unsafe train movement is defined for both MB and VC capturing functional interactions and cause-effect relations among the different signalling components. The FTA is used as a basis to apportion signalling component failure rates needed to feed the SAN model. Effective MB and VC train supervision is analysed by means of SAN-based simulations in the specific scenario of an error in the Train Position Report (TPR) for five rail market segments featuring different traffic characteristics, namely high-speed, mainline, regional, urban and freight. Results show that the thresholds of the design variables depend on the considered signalling system alternative and the investigated market segment. In particular, the TPR delay threshold allowed for MB is higher than for VC. This means that to ensure a safe train movement, VC cannot absorb a TPR delay of longer than 1.5 s, which corresponds to the mainline market segment. For MB instead, the results show that the maximum TPR delay can reach 3.9 s for high-speed and freight railways. In addition, results showed that the integration of an FTA in a SAN model can provide a better understanding of the safety-performance behaviour of a system where VC showed a higher number of braking indications with respect to MB for the same TPR error failure rate. This means that for VC to effectively supervise the train separation at the same safety level as MB, we would need to have a much higher reliability of the TPR. The overall approach can support infrastructure managers, railway undertakings, and rail signalling suppliers in investigating the effectiveness of MB and VC to safely supervise train movements in scenarios involving different types of degraded conditions and failure events. The proposed method can hence support the railway industry in identifying effective and safe design configurations of next-generation rail signalling systems.

A Comprehensive Analysis of the Reliability of Electric Vehicle Motor Systems: Exploring the Intricacies of Performance and Durability

Electric vehicles (EVs) have garnered significant attention in recent years due to their distinctive zero carbon emission capability. While their environmental advantages are widely acknowledged, concerns about the reliability of key components, particularly the drive motors and associated controllers, continue to linger. Previous studies have made strides in evaluating the reliability of individual drive motors in EVs. However, given the integrated nature of drive motors and motor controllers in EVs, a comprehensive assessment that considers both components as a unified system is imperative for more precise reliability forecasting. Furthermore, the multifaceted nature of drive motors and motor controllers, encompassing various components with diverse structures, types, and characteristics, can significantly influence the overall reliability of the motor system. Unfortunately, these nuances have often been overlooked in prior research, creating a knowledge gap that needs to be addressed. To address this gap, our study offers a holistic investigation into the reliability of the entire motor system in pure electric vans, which includes both the drive motors and motor controllers. We initiate our research by employing theoretical models to predict the failure rates of individual subassemblies and components within the drive motor and motor controller. Building upon these predictions, we conduct an in-depth assessment of the overall motor system reliability. Our findings reveal the vulnerabilities associated with specific subassemblies and components within the motor system, offering critical insights that challenge some of the prevailing assumptions in existing reliability research. These insights are instrumental in guiding future advancements in reliability design and maintenance practices tailored for pure electric vans. The adoption of EVs continues to grow, driven by their environmental benefits and expanding market share, it becomes increasingly crucial to address concerns related to the reliability of essential components like drive motors and control systems. While previous research has largely focused on component-level reliability, our study adopts a more comprehensive approach, evaluating the entire motor system. Through a combination of theoretical modeling and empirical analysis, we aim to provide a robust framework for predicting failure rates and enhancing the overall reliability of EV motor systems. By uncovering vulnerabilities and potential areas for improvement, this research offers valuable insights to advance the design, development, and maintenance of reliable electric vehicles.

An Exhaustive Examination of the Motor System’s Reliability in Electric Vehicles

Due to their unique zero carbon emission feature, electric vehicles (EVs) have garnered increasing interest in recent years. However, concerns persist regarding their reliability, particularly concerning critical components. Efforts have been made to assess the reliability of drive motors in EVs. Yet, given the integration of drive motors and motor controllers as a single system in EVs, a combined assessment is essential for a more accurate reliability prediction. Additionally, considering the multiple components within both drive motors and motor controllers, their structure, type, and characteristics may influence the overall reliability of the motor system, a factor overlooked in previous research. To bridge this knowledge gap, this study investigates the reliability of the entire motor system in pure electric vans, encompassing both drive motors and motor controllers. The research begins with predicting the theoretical failure rates of subassemblies and components within the drive motor and motor controller. Subsequently, based on these predictions, the reliability of the entire motor system is assessed. The findings highlight vulnerable subassemblies and components within the motor system, shedding light on potential shortcomings in existing reliability research. These new insights are deemed crucial for informing future reliability design and maintenance practices for pure electric vans. This study undertakes an exhaustive investigation into the reliability of the motor system in EVs. As EVs gain prominence for their environmental benefits and increasing market share, concerns regarding the reliability of critical components, such as the drive motor and associated control systems, become paramount. While prior research has addressed component-level reliability, this study takes a holistic approach by assessing the entire motor system. Through theoretical modeling and empirical analysis, the study predicts failure rates of subassemblies and components within the drive motor and control systems, culminating in an evaluation of the overall motor system reliability. By uncovering vulnerabilities and potential areas for improvement, this research offers valuable insights to advance the design, development, and maintenance of reliable electric vehicles.

Parameter synthesis for Markov models: covering the parameter space

Markov chain analysis is a key technique in formal verification. A practical obstacle is that all probabilities in Markov models need to be known. However, system quantities such as failure rates or packet loss ratios, etc. are often not—or only partially—known. This motivates considering parametric models with transitions labeled with functions over parameters. Whereas traditional Markov chain analysis relies on a single, fixed set of probabilities, analysing parametric Markov models focuses on synthesising parameter values that establish a given safety or performance specification φ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\varphi $$\\end{document}. Examples are: what component failure rates ensure the probability of a system breakdown to be below 0.00000001?, or which failure rates maximise the performance, for instance the throughput, of the system? This paper presents various analysis algorithms for parametric discrete-time Markov chains and Markov decision processes. We focus on three problems: (a) do all parameter values within a given region satisfy φ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\varphi $$\\end{document}?, (b) which regions satisfy φ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\varphi $$\\end{document} and which ones do not?, and (c) an approximate version of (b) focusing on covering a large fraction of all possible parameter values. We give a detailed account of the various algorithms, present a software tool realising these techniques, and report on an extensive experimental evaluation on benchmarks that span a wide range of applications.

Reliability and Mean Time to System Failure of a Parallel System' by Using One or Two Decimal Random Data Points

Focusing on Weibull failure rules, which govern the stopping of components, this work evaluates reliability metrics such as stability and the mean time to system failure (MTSF) of a structure that is parallel. These metrics' behaviour has been seen for one or two decimal random values of component failure rates, operation times, form parameters, and the total quantity of components used in the parallel structure. In order to analyze the variation in the ethics of reliability as well as MTSF, the particular case of the Weibull distribution has also been taken up.

RETRACTED: Data–Driven Adaptive Fault–Tolerant Control for Floating Offshore Wind Turbines

Floating offshore wind turbines have increased in popularity owing to their adaptability for deep-water applications and high power generation efficiency. The control of floating offshore wind turbines, on the other hand, is very complex. The main challenges are the difficulty in precisely modelling floating offshore wind turbines and the higher failure rate of components. As a consequence, this study proposes a model-free adaptive fault-tolerant control system for blade root moment sensor failures. A model-free adaptive control approach is used to construct an individual pitch controller and a fault compensation to avoid mathematical modelling of floating offshore wind turbines. The proposed fault-tolerant control technique removes the need for fault detection and isolation by converting the fault dynamic compensation process into a real-time control issue for nonlinear systems. The fatigue, aerodynamics, structures, and turbulence code simulates and tests the proposed control strategy, and the results show that the proposed strategy can not only keep the wheel bearing load balanced but also reduce the movement of the floating platform and significantly reduce the bearing load of the floating offshore wind turbines. Furthermore, the output power is closer to the rated power, proving the strategy’s high fault tolerance in the face of repeated blade root moment sensor faults.

Stability and reliability analysis of a non‐isolated high gain DC‐DC converter

AbstractThis paper investigates the stability and reliability analysis of a non‐isolated high‐gain DC‐DC converter. The proposed DC‐DC converter produces a high gain output voltage with a lower duty ratio and reduced voltage stress on the semiconductor switch. A reliability analysis is performed to predict the failure rate and lifetime of the individual components using the military handbook (MIL‐HDBK‐217F). A state space equation and small signal modeling of the proposed converter are derived to elucidate the performance of the proposed converter through stability analysis. A closed‐loop using PID controller is incorporated to attain the constant regulated output voltage during sudden parameter changes. The validation of the closed‐loop using the PID controller is verified with the wide range of variations in the different parameters, such as input voltage, load value, and reference voltage value. The laboratory‐based prototype is developed for the proposed converter and validated the performance with 250 W. The power density and efficiency of the proposed converter are 1.360 kW/L and 93%, respectively.

Prediction of Remaining Useful Life (RUL) of Electronic Components in the POSAFE-Q PLC Platform under NPP Dynamic Stress Conditions

In the Korean domestic nuclear industry, to analyze the reliability of instrumentation and control (I&C) systems, the failure rates of the electronic components constituting the I&C systems are predicted based on the MIL-HDBK-217F standard titled ‘Reliability Prediction of Electronic Equipment’. Based on these predicted failure rates, the mean time to failure of the I&C systems is calculated to determine the replacement period of the I&C systems. However, this conventional approach to the prediction of electronic component failure rates assumes that factors affecting the failure rates such as ambient temperature and operating voltage are static constants. In this regard, the objective of this study is to propose a prediction method for the remaining useful life (RUL) of electronic components considering mean time to failure calculations reflecting dynamic environments, such as changes in ambient temperature and operating voltage. Results of this study show that the RUL of electronic components can be estimated depending on time-varying temperature and electrical stress, implying that the RUL of electronic components can be predicted under dynamic stress conditions.

Reactor reliability modeling and reliable life analysis method for multi-state space reactor systems based on DBN and interval estimation

Space nuclear reactor power (space reactor) is of the highest energy density and the most promising long-term self-sustaining and high-power level space power source. Achieving high reliability during the years-long maintenance-free service process is one of the bottlenecks that constrain the application of space reactors, which brings a challenging task on the quantitative evaluation of reliability indicators (life and reliability) of space reactors during the design phase. On the one hand, traditional binary reliability theory cannot model the system functional structure accurately, due to diverse failure modes and complex propagation paths; on the other hand, the lack of reliability data introduces large epistemic uncertainty, resulting from high costs and time-consuming ground reliability tests. To tackle these problems, this paper takes the Stirling integrated space reactor (ACMIR) as a specific research object and proposes a general reactor reliability modeling and reliable life analysis method for space reactors with large prior epistemic uncertainty which is robust to manipulation and can obtain system reliability indicators with small confidence intervals for engineering decisions. First, based on experts’ experience, the functional logic relationship between components and system is sorted out by the failure mode and effect analysis (FMEA) and the prior failure rates of components are evaluated according to the failure grade of the industrial standard. Then, the FMEA model is mapped to a discrete-time Bayesian network (DBN) to model the system reliable life, based on which system reliability indicators with low epistemic uncertainty are analyzed by interval estimation. Finally, the practical and engineering value of the proposed method is demonstrated with the example of ACMIR in two application cases.

A Network-gene-mutation-based model for large network dynamic application reliability evaluation

Network evolution model (NEM) is a framework proposed recently for dynamic application reliability evaluation which integrates various factors that affect application failure. The existing component-based NEM relies on an exhaustive enumeration of all potential states of network components, which can lead to a computational bottleneck, especially when dealing with large networks. Therefore, to tackle this challenge, this paper presents a Network-gene-mutation-based NEM. The model introduces novel concepts of Network-cells and Network-genes inspired by the gene-cell-function model framework used in system biology to address complex disease problems. Network-cells are constructed according to the statistical classification of components. Network-genes are constructed by the average failure rate of components within a Network-cell. Dynamic network states are simulated by the Network-gene-mutation function of Network-cells instead of the failure and repair function of network components. Validation of our proposed model is performed through the comparison with benchmark models. The results demonstrate the validity of our proposed model in calculating dynamic application reliability. Besides, the comparison results with the component-based NEM also suggest that our proposed model reduces the time complexity of the algorithm from square order [Formula: see text] to constant order [Formula: see text] and improves the simulation efficiency by roughly 104. Applied to networks of varying scales, our proposed model proves to be an efficient method for calculating dynamic application reliability in large networks. In summary, this paper offers a promising solution to address the computational bottlenecks encountered in the component-based NEM when evaluating the reliability of dynamic applications.