Failure Probability Of Components Research Articles

A model for operational risk of industrial facilities including ageing/test degradation effects and maintenance effectiveness

The paper presents a model for operational risk of industrial facilities with applications to nuclear power plants and uranium mining. The traditional models update the risk profile for specific operational conditions setting to one the failure probability of components which are known to be unavailable by failure or maintenance, while constant mean unavailability values from the original Probabilistic Safety Analysis (PSA) are kept for the rest. The proposed methodology considers the time dependency of standby failure probabilities through instantaneous unavailability models, depending on the specific standby time for each component at the given moment, instead of using the same constant values. It also incorporates ageing, testing degradation effects and maintenance effectiveness, not explicitly considered in traditional models, as well as the instantaneous reevaluation of common cause failure probabilities. The results show significant risk underestimations when components specific standby times, ageing, testing degradation effects and maintenance effectiveness are not considered.

Computationally efficient Complete-Sampling-based fault tree analyses for seismic probabilistic safety assessment of nuclear facilities

The seismic risk of nuclear power plants (NPPs) can be assessed through seismic probabilistic safety assessment (PSA) based on fault tree analyses (FT). The conventional FT-based approach has limitations for handling the correlated relation of basic events and utilizing the sampling-based FT analysis for addressing such issues demands considerable computational cost. In particular, accurate consideration of the partial dependences between the failure probabilities of NPP components in seismic hazard events can further increase the computational cost of risk quantification. Therefore, this paper presents an improved complete-sampling-based seismic PSA risk quantification method. The novelty of this method is that it increases the efficiency of the risk quantification by introducing sequential sampling and truncation to the conventional fault tree analysis. In addition, as sub-concepts, we eliminate unrequired sampling extraction steps via integrated multivariate response distribution-based sampling for components and continual evaluation of the convergence of system failure probabilities. On three considered examples, including an actual NPP, the proposed method requires 91%–96% fewer samples than the conventional method, without sacrificing accuracy. These results indicate the efficiency of the proposed method, which is expected to become a useful tool in the future.

Fault diagnosis of the distribution network based on the D-S evidence theory Bayesian network

Relay protection rejection and misoperation exist in the existing distribution network, which will affect the fault diagnosis results. To diagnose faults in distribution networks, this paper presents a fault diagnosis method for the distribution network based on the D-S evidence theory Bayesian network. First, the collected relay protection information is divided into two categories, protection information and circuit breaker information; the corresponding Bayesian network model is established based on their respective action logic, and the corresponding component failure probability is obtained by Bayesian backward inference. Second, the fault probabilities obtained from the two Bayesian networks are fused by the D-S evidence theory, and the obtained fault probabilities are used to diagnose the faulty component. Then, using the Bayesian network corresponding to the faulty component to perform Bayesian forward inference, the protection devices and circuit breakers are identified for misoperation or rejection to achieve the fault diagnosis of the distribution network. Finally, the correctness and reliability of the proposed diagnosis method are verified through the analysis of arithmetic cases.

A fuzzy reliability assessment methodology for city gas stations based on an extended T-S fault tree

City gas stations (CGSs) play a crucial role in ensuring a stable and safe supply of natural gas to urban users. However, as the service time of stations increases and the performance of components deteriorates, concerns about the safety and reliability of these station have grown among operators and local government authorities. This paper proposes a fuzzy reliability assessment methodology for CGSs that considers the polymorphism of component faults and the uncertainties associated with fault relationships, failure probabilities, and fault magnitudes. The methodology utilizes T-S fuzzy gates to describe the correlation among events and constructs a T-S fuzzy fault tree for CGSs. Component fault states are represented using fuzzy numbers, and a fuzzy group decision-making approach is introduced to evaluate the current fault magnitude of components. To handle the uncertainty caused by sparse failure sample data, a Bayesian updating estimation method is presented to estimate the failure probabilities of components. Furthermore, T-S fuzzy importance analysis is applied to identify the weak points in the CGS system. The effectiveness of the developed methodology is demonstrated through a case study of reliability analysis of a city gas distribution station. The research findings provide valuable support for optimizing the design and implementing preventive maintenance of CGSs.

Evaluation of primary frequency regulation in Dominican Republic mine using scooptrams and electric trucks as battery storage systems

The use of electric vehicles in the mining sector aligns with the growing demand for tools that reduce energy consumption in productive processes. This study investigates how electric mining vehicles with vehicle-to-grid (V2G) technology can be integrated into power grids to improve system stability and frequency regulation. In the context of mining operations and the provision of auxiliary services (such as primary frequency regulation) to an interconnected electrical system, a regulatory framework that enables V2G integration should be established. This issue is addressed through a case study involving DIgSILENT PowerFactory simulations. The goal is to evaluate the productive process of a mine and the contribution of V2G technology to the primary frequency regulation of the electrical system under different scooptram integration parameters and battery electric trucks. This study also explores the challenges of V2G implementation in the absence of a regulatory framework. Different configurations of electric scooptrams and shovel that can affect frequency regulation are assessed. The obtained optimal configuration involves two truck loaders and three shovel loaders; the capacity of the current electrical infrastructure can support this configuration without affecting the capacity of the mine's electrical system. The simulations verify that applying V2G technology to mining vehicles helps stabilise the frequency of electrical systems and reduces the probability of component failure.

AI-enabled damage estimation of hurricane-impacted residential communities through CFD simulations and stratified sampling

This study introduces a novel framework for estimating hurricane-induced damage to individual buildings by integrating AI-based 3D building modeling, Computational Fluid Dynamics (CFD), and stratified sampling. By leveraging the precise geometric characteristics of individual buildings extracted from digital data, the framework enables the accurate computation of pressure distributions for individual buildings through high-fidelity CFD simulations. The subsequent integration of an AI-based building component detection technique with vulnerability modeling and enhanced stratified sampling enables the rapid computation of the component and building-level failure probabilities. Applied to a case study in Atlantic City, NJ, the research underscores the effectiveness of realistic geometric characterizations and the inclusion of neighboring buildings in estimating individual-building level damage. Unlike conventional archetype-based approaches, the proposed methodology offers individual-building level risk assessment, reflecting the crucial role played by geometric and aerodynamic variables. The framework offers a promising step towards automated high-fidelity assessments of hurricane risks, leveraging detailed modeling and simulation to contribute to more resilient communities.

Reliability-based maintenance optimization of long-distance oil and gas transmission pipeline networks

This paper presents a maintenance model for optimal planning of pipeline network inspections subjected to corrosion and residual stress. The proposed model includes predictive degradation modeling, spatio-temporal reliability, and expected cost minimization. The pipeline networks are formed by linear segments and irregular zones such as elbows, weld joints, and flanges for putting pipes together and changing the direction of gas or liquid flow. The mean of Karhunen-Loève expansion is used to model space-variant corrosion, residual stress, and stochastic dependency in different exposure zones. Monte Carlo simulations are employed to compute the failure probability of individual components. Subsequently, a Bayesian network evaluates the failure probability of the complex pipeline network and identifies the most critical components. Depending on the objective or market demand uncertainties, a cost model is also proposed to optimize the inspection policy. The methodology is applied to a case study, and the results show that it can provide valuable insights to decision-makers to enhance complex systems' safety and reliability.

System reliability analysis of Cable-stayed bridge using pair Copula and ICLHS with unequal weights under seismic loading

Considering the correlation between different components, quantifying the failure probability of a high-level hyper-static structure under non-stationary seismic loading is of great significance in guiding earthquake-resistant design and fortification of bridges. In this study, a high-pier and large-span railway cable-stayed bridge in the mountainous region were employed to evaluate the seismic performance. First, the basic theoretical methods of the non-stationary random seismic motion simulation, equivalent extreme value theory based on extreme value distribution (EVD), improved Latin hypercube sampling (ICLHS) with unequal weighted fractional moment estimation and Pair Copula technique are presented. Second, the finite element analysis model of the cable-stayed bridge was established based on the OpenSees platform, the seismic response samples of the bridge were calculated under three levels of seismic ground motion to determine the first passage failure probability of the main components of the bridge under the design failure modes. Finally, the Pair Copula function was introduced to estimate the seismic system reliability of the cable-stayed bridge. The optimal Copula type that can describe the correlation between the bridge components was selected through goodness-of-fit tests such as AIC, BIC, and the minimum distance method. The failure probability of the entire system was obtained by calculating the selected Copula function type under seismic loading.

System reliability modeling and analysis for a marine power equipment operating in a discrete‐time dynamic environment

AbstractExploring on reliability modeling and analysis on a marine equipment in a dynamic environment is a meaningful and challenging issue, because the system commonly carries out the task at sea away from land and suffers a distinct influence of environment. Thus, a reliability model of a multi‐state repairable system operating in dynamic environment is developed by introducing the background of the marine power system in this paper. The novelty of the research lies in the modeling and computing methods are relatively innovative by employing the aggregated stochastic processes, Hadamard production and matrix‐analytic method. First, the working modes of the marine power system under several different kinds of conditions are introduced. Then, the evolution of both the system states and environment are described as discrete‐time Markov chains with multiple and different transition probability matrices. The failure probability and repair probability of components are also distinct in different environments. Furthermore, some performance indexes, especially the index relevant to the environment, are derived, respectively. Finally, the conclusion is obtained by a numerical example of the marine power system, which also illustrates the validity and applicability of the proposed model.

MATHEMATICAL MODEL OF RELIABILITY ASSESSMENT FOR DIAGNOSING THE TECHNICAL CONDITION OF ELECTRICAL ENGINEERING COMPLEXES IN DISTRIBUTED WATER SUPPLY SYSTEMS

Distributed water supply systems are high-tech complexes that include a variety of electrical components and control systems. Ensuring their reliable and uninterrupted operation requires continuous monitoring and diagnostics of their technical condition. The diagnosis of distributed water supply systems depends on many factors, which can be both deterministic and random. At present, this is a complex task due to the diversity of processes affecting the parameters of the water supply network's operational state. The non-stationarity of data, caused by cyclic changes in flow rate and pressure over time, also makes their diagnosis challenging. For precise diagnosis of the state of distributed water supply systems, it is necessary to consider not only typical emergency situations but also anomalous water consumption conditions and peculiarities of pressure management system operation. The article proposes a mathematical model for assessing the reliability of electrical engineering complexes in distributed water supply systems for the purpose of their diagnostics. The model is based on the analysis of failure probabilities of various components and subsystems of the complex. The research aims to help identify problematic areas in the water supply system and prevent potential emergency situations. The research results can be used to improve the safety and efficiency of water supply systems, as well as to optimize resource management. The use of the mathematical model in conjunction with modern diagnostic technologies can significantly improve the management of water supply infrastructure and ensure the reliable operation of electrical complexes. A convenient indicator for assessing the diagnosis of the technical condition of electrical engineering complexes in distributed water supply systems is the probability of failure downtime, which reflects both the frequency of failure occurrence and the duration of downtime. For assessing the reliability of the consumer water supply system, it is expedient to use such an indicator: the probability of safe downtime, which takes into account both the frequency of failures and the permissible duration of downtime under safety conditions.

Reliability analysis of subsea control module based on dynamic Bayesian network and digital twin

The reliability evaluation of the subsea control module(SCM) is the key to ensure the safety and stability of subsea oil-gas production. The failure probability and reliability of SCM components are dependent on time and working conditions. To analyze the SCMs’ reliability considering varying working conditions, this paper proposed a new digital twin and dynamic Bayesian network(DBN) based model utilizing historical working condition data in reliability analysis. In the proposed framework, critical working condition data is obtained by sensor-based Digital Twin(DT) simulation and used for dynamically updating the parameters in the DBN reliability analysis model. The reliability evaluation of an actual SCM electric system was carried out. The results revealed the most probable failure mode and the most vulnerable components in the system. Finally, the fault prediction based on the back-forward analysis capacity of the proposed method was conducted to predict the probability of the faulty device when unexpected failures occur.

Data-Driven Fatigue Failure Probability Updating of OSD by Bayesian Backward Propagation

This study introduces a data-driven approach for updating the fatigue failure probability of the orthotropic steel deck (OSD) using Bayesian backward propagation. The OSD in steel bridges is considered as a parallel system composed of two critical fatigue-prone components, namely, the rib-to-diaphragm and rib-to-deck joints. A probabilistic model for fatigue reliability is established based on the equivalent structural stress method and limit state function. The system-level fatigue reliability model is then constructed, taking into account the correlations between limit states of individual components through Bayesian network forward propagation. The key advantage of the Bayesian network-based framework is its ability to perform backward propagation, allowing for the updating of failure probabilities for critical components when the system-level failure of the OSD is observed. Consequently, the proposed approach enables the identification of vulnerable components through data-driven fatigue failure probability updating. Finally, the approach is applied to a real instrumented steel bridge to determine the time-dependent fatigue failure probability at both the system and component levels over its service life. The results show that the component-level fatigue failure probability model will underestimate the fatigue life in comparison to the system-level model. Meanwhile, the proposed method could identify vulnerable components by quantifying the fatigue failure probability of in-service steel bridges.

Field teams coordination for earthquake-damaged distribution system energization

The re-energization of electrical distribution systems in a post-disaster scenario is of grave importance as most modern infrastructure systems rely heavily on the presence of electricity. This paper introduces a method to coordinate the field teams for the optimal energization of an electrical distribution system after an earthquake-induced blackout. The proposed method utilizes a Markov Decision Process (MDP) to create an optimal energization strategy, which aims to minimize the expected time to energize each distribution system component. The travel duration of each team and the possible outcomes of the energization attempts are considered in the state transitions. The failure probabilities of the system components are computed using the fragility curves of structures and the Peak Ground Acceleration (PGA) values which are encoded to the MDP model via transition probabilities. Furthermore, the proposed solution offers several methods to determine the non-optimal actions during the construction of the MDP and eliminate them in order to improve the run-time performance without sacrificing the optimality of the solution.

A Bayesian Deep Learning-Based Probabilistic Risk Assessment and Early-Warning Model for Power Systems Considering Meteorological Conditions

The ongoing process of decarbonizing the power system and the frequent occurrence of extreme weather have led to a significant increase in operating uncertainty and greatly reduced the system controllability. An effective risk assessment and early-warning tool will significantly assist system operators in monitoring and controlling power systems. However, existing methods mainly rely on simplified analytical conditional probability models to describe the component failure probability under different operating conditions and are not suitable for low-probability risk assessment. Inspired by the idea of Bayesian statistics, a Bayesian deep learning-based probabilistic risk assessment and early-warning (BDL-PRAEW) model considering meteorological conditions is proposed in this paper. A new Bayesian neural network (BNN) is proposed which efficiently utilizes expert experience and domain knowledge as prior to model the contingency probability. And a hybrid neural network is developed to rationally utilize the multi-source heterogeneous data and comprehensively analyze the historical and forecast information. Finally, a novel risk assessment and early-warning model for high-impact, low-probability (HILP) extreme events is proposed, which can predict the risk for the next n time-steps. The proposed method is tested on a modified IEEE 118-bus system and a real-world provincial grid, and the results verify its effectiveness.

Computationally efficient approach for risk-informed decision making

Probabilistic risk assessment (PRA) is used as an essential tool for risk-informed decision-making in the nuclear industry. The fault and event trees play a crucial role in PRA to estimate the probability of system failure based on the failure probabilities of components. The fault trees or event trees for an actual power plant unit can be fairly large in size with several different types of logic gates, interconnected events, dependent events, etc. A large fault tree can include hundreds of gates, basic events (BEs), multiple occurring events (MOEs), and dependent events. Complex connectivities can give rise to excessive computational demand and storage requirements for the analysis. Fault and event trees can be solved using the minimal cut-set approaches, or advanced quantification techniques such as Binary decision diagrams or Bayesian networks. However, these techniques can be computationally inefficient for larger fault trees and can run out of memory/storage space. This study focuses on developing and proposing a new approach for accurate estimation of the system-level risk while improving the computational efficiency significantly. More specifically, an attempt is made to reduce the complexity of the analysis of MOEs and dependent events in fault trees. The proposed algorithms in this study present a significant improvement over traditional approaches which makes it highly promising for additional development. The computational efficiency of the proposed approach over the traditional approach is illustrated for fault trees with a varying number of events and different types of logic gate connections.

Failure probability analysis of hydrogen doped pipelines based on the Bayesian network

Damage from hydrogen, such as hydrogen embrittlement in natural gas pipelines, is a possibility. There are currently lacking ways for calculating the failure of hydrogen doped pipelines quantitatively; instead, research on the safety of them is mostly concentrated on hydrogen compatibility tests and consequence analysis. A method for quantifying hydrogen doped pipelines losses using Bayesian network-based failure probability analysis is presented. The influence of hydrogen on pipeline failure is introduced using the expert scoring method based on the analysis of historical failure data for gas pipelines. The failure probabilities of various components of hydrogen doped pipelines are then quantified by combining fuzzy mathematical theory and hierarchical analysis, which is subsequently utilized as prior probabilities to input into the Bayesian network for quantitative calculation. The results show that the Bayesian network can more accurately quantify the failure probability of hydrogen doped pipelines. They also show that the Bayesian network can provide a practical approach for quantitative risk assessment and safety evaluation, as well as a decision basis for the prevention of hydrogen doping pipeline failure accidents.

Enhancing Electric Vehicle Reliability and Integration with Renewable Energy: A Multi-Faceted Review

Electric vehicles (EVs) are gaining attention due to their zero carbon emissions, but concerns about their reliability, especially critical components, persist. Previous research has primarily focused on EV drive motor reliability, neglecting the motor controller. To address this gap, this study assesses the reliability of the entire motor system in electric vans, including both drive motor and motor controller components. It predicts failure rates for these components, highlighting vulnerabilities and shortcomings in existing research, which can inform future design and maintenance. In addition, the integration of EVs and renewable energy resources has garnered attention, but concerns about component reliability have arisen. A novel approach called " Innovative Incentive-Driven Fuzzy Fault Tree Analysis " (IIFFTA) is introduced for power systems incorporating EVs and renewable energy, addressing vague and imprecise events and data deficiencies in conventional fault tree analysis. IIFFTA considers different component failure rates and probability values of fault occurrences, offering a more effective risk assessment method. Lastly, plug-in electric vehicles (PEVs) impact distribution systems, and this paper explores distribution feeder reconfiguration (DFR) as a reliability-enhancing strategy for coordinating vehicle-to-grid (V2G) services from PEV fleets within a stochastic framework. The study accounts for uncertainties related to network demand, energy prices, wind power generation, and PEV fleet behavior, employing a self-adaptive evolutionary algorithm (SOS) to address the stochastic optimization problem.

Study on creep damage and life prediction of the planar solid oxide fuel cell by modeling of multiphysics coupled

In present paper, a strain-based creep damage and failure probability model is constructed to conduct time dependent mechanical performance analysis of solid oxide fuel cell (SOFC) with nonuniform temperature fields obtained by multiphysics coupled modeling at an operational temperature of 800 °C. It demonstrates that assuming uniform temperature fields significantly underestimates the creep damage and failure probability of SOFC components compared to nonuniform temperature fields. The upper interconnector reaches the critical damage value at 54 117 h and initiates a crack, located on the outer surface of the upper interconnector rib, positioned 39 mm from the fuel outlet. After 55 000 h, the failure probability of the anode support is rapidly increasing, reaching 0.185 after 80 000 h, which is 5–6 orders higher than other components. The upper interconnector and anode support are potential failure components, which should be subject to safety inspection to ensure structural integrity after 55 000 h of operation.

Fuzzy fault tree analysis of EVAC system based on improved SAM- FFTA with butterfly optimization algorithm

At this stage, 3D elliptical vibration-assisted cutting(3D-EVAC) systems have proven to be a highly promising method of machining for a wide range of new materials, which can meet the requirements of ultra-precision machining. In practice, however, there are still various problems that affect its ability to achieve the desired accuracy. The functional reliability of the system should therefore be improved. Considering that the failure probability of 3D-EVAC system components cannot be obtained accurately, this paper proposes an improved similarity aggregation method (SAM) based on the butterfly optimization algorithm (BOA), called BOA -SAM. In the experiment, thirty-six events are selected and these events are aggregated by BOA-SAM. The experimental results show that BOA-SAM not only avoids the variability of the aggregation results with the relaxation factor (β) but also improves the accuracy of the aggregation results compared with SAM. BOA-SAM and FTA are connected by the center-of-mass method to form BOA-SAM-FFTA. Finally, a fuzzy fault tree analysis of the 3D-EVAC system was carried out using the BOA-SAM-FFTA method to calculate the probability of failure for each bottom event and to analyze its importance. It was found that non-uniform heating of the tool was the event that had the greatest impact on the functional reliability of the system, which offered a theoretical basis for improving the functional reliability of the system. The probability of failure of other bottom events during the experiment also can provide some theoretical guidance for the system maintenance plan .

PDEM-based multi-component and global reliability evaluation framework for steel tubular transmission towers with semi-rigid connections

The conventional analysis and design of transmission towers usually ignore the effect of semi-rigid behaviors of bolted joints and simplify the joints as the rigid or pinned connection, which might overestimate or underestimate the structural response of transmission towers and the reliability of structural components and systems. To consider the effect of semi-rigid connections on the reliability of transmission towers in this paper, a stochastic dynamic finite element model of steel tubular transmission towers with semi-rigid connections (STTTs-SRCs) is established by deriving their stiffness matrix, where the uncertainties of dimensions, material and semi-rigid connections are considered and the wind loads are simulated as stochastic processes by the dimension-reduced probabilistic simulation approach. Then, based on the equivalent extreme-value event, the multi-component and global performance functions with stress and displacement failure modes for STTTs-SRCs are established, respectively. Further, a reliability evaluation framework for STTTs-SRCs is proposed based on the probability density evolution method (PDEM). Finally, a practical engineering transmission tower is regarded as an example to demonstrate the implementation process of the proposed reliability evaluation framework. Moreover, the reliability of steel tubular transmission towers with rigid and pinned connections is compared with that of STTTs-SRCs to investigate the effect of semi-rigid connections on the reliability of transmission towers. In general, the semi-rigid connections would mainly affect the value of the multi-component failure probability, however, it would not change the location where the maximum failure probability of components occurs. Additionally, the semi-rigid connection significantly affects the global failure probability of towers for the stress failure mode while slightly affects the global failure probability of towers for the displacement failure mode. Therefore, it may be more appropriate to consider the semi-rigid connections into the analysis and design of steel tubular transmission towers.