Reliability Assessment Method Research Articles

Bayesian improved cross entropy method for network reliability assessment

We identify the zero count problem (or overfitting) of cross-entropy-based methods in the context of network reliability assessment, and propose a consistent Bayesian estimator that mitigates this issue. Thereby, we derive the posterior predictive distribution of importance sampling distribution parameters that replaces the weighted maximum likelihood estimation employed in the standard cross entropy optimization. For rare event estimation, we embed the Bayesian estimator into the improved cross entropy (iCE) method and provide theoretical insights into the adaptive selection of intermediate target distributions in the iCE. The modified version of the iCE, termed the Bayesian iCE (BiCE), is proved to be unbiased. By contrast, even with higher computational cost, the standard iCE method is often significantly biased when solving network reliability problems. Our numerical investigations indicate that a uniform prior in the proposed BiCE method performs suboptimally, and an informative symmetric Dirichlet prior is suggested.

An evidential network approach to reliability assessment by aggregating system‐level imprecise knowledge

AbstractModern complex engineering systems oftentimes possess hierarchical structures which can be physically divided into several levels. Traditional reliability assessment methods were conducted using field data or time‐to‐failure data. For a hierarchical system, different levels’ reliability‐related data from monitoring or observations can provide additional information to update the system's reliability. However, with the limitation of sensing and monitoring technologies and vague judgments from experts, the reliability data inevitably contain epistemic uncertainty. Properly fusing the imprecise data can still improve the accuracy of system reliability assessment. In this article, we propose a reliability assessment approach based on the evidential network (EN) model to update the hierarchical systems’ reliability by aggregating the system‐level imprecise knowledge. First, the evidence theory is applied to quantify the epistemic uncertainty associated with the imprecise system‐level reliability data. Then, an EN model is created to realize the uncertainty propagation and fusion of imprecise data. Finally, at any particular time instant, the system reliability can be updated with the aid of imprecise data based on the proposed EN model. The effectiveness of the proposed method is validated by a real engineering case of a wind turbine system. Overall, the results shown from the case study support that the proposed method can update the reliability of hierarchical systems by using system‐level imprecise data.

An Efficient Decoupled Reliability-Based Topology Optimization Method Based on a Performance Shift Strategy

AbstractIn this paper, a new efficient reliability-based topology optimization (RBTO) method is proposed for structures, in which the double-loop optimization is equivalently decoupled into a sequential process based on a performance shift strategy. First, a volume minimization RBTO formulation is built for structures considering displacement or compliance reliability constraints. Second, an efficient decoupling scheme is proposed to turn the double-loop RBTO into a series of deterministic topology optimization and reliability analysis. For the reliability analysis, the reliability probability is calculated based on the probability distribution function of the performance function, and the probability distribution function can be solved by the maximum entropy method based on the raw moments calculated by the multiplicative dimension reduction method. A performance shift strategy is then applied to build an equivalence between probabilistic constraint and deterministic constraint to formulate the deterministic topology optimization. Thirdly, the adjoint variable method is applied to obtain the sensitivity information for topological design variables, and a gradient-based optimization algorithm is used to update the design variables. Finally, four typical numerical examples are used to verify the advantage of the proposed method. Compared with the traditional sequential optimization and reliability assessment (SORA) method, the proposed RBTO method results in a design with a 5.4% smaller volume value to reach the same reliability index requirement for the cantilever beam, and the function calls are 354 times less than that of the SORA method.

Reliability Assessment of Cyber–Physical Distribution Systems Considering Cyber Disturbances

With the development of communication technology, traditional distribution networks have gradually developed into cyber–physical systems (CPSs), from which the cyber system provides more protection for the grid and brings new security threat–cyber disturbances. Current research cannot scientifically measure the impact of cyber disturbances on the system and lacks reliability indices for a comprehensive quantitative assessment of CPS reliability from the perspective of cyber–physical fusion. If the impact of information disturbances on system reliability is not assessed accurately, it will not be possible to provide a scientific and reasonable decision basis for system planning and operation. Therefore, a set of reliability assessment methods and indices for distribution network CPSs considering cyber disturbances is proposed. Firstly, a reliability modeling method combining fault tree and Petri net is proposed to model the reliability of a distribution network CPS, which can improve the efficiency and accuracy of the modeling. Secondly, the system state is divided into two categories: normal operation state and cyberattack state. Then, generalized reliability indices considering cyber disturbances for distribution network CPSs are defined. Finally, through the tests on the modified IEEE RBTS BUS2 distribution network CPS, an analysis of the effects of information component failures, cyberattacks, and access network structures on system reliability is conducted in this paper to verify the efficiency of the proposed method and the rationality of the newly defined reliability indices.

Reliability assessment method based on the meta-action unit for complex mechanical system

Reliability assessment method based on the meta-action unit for complex mechanical system

RETRACTED ARTICLE: Research on reliability evolutionary optimization of relays device based on Multi-Markov model

As a critical component of the present day power system, a few studies have been conducted on the optimization and future prediction for quality reliability of relay protection devices during their production stage of intelligent testing. Therefore, in this study, a Markov model of multimodal hierarchical spatial states is proposed (1) to calculate the stationary probability and comprehensive availability of different spatial state transfers (2) to predict the quality reliability of the device during production intelligence testing and (3) to infer and calculated the integrated availability of the relay protection device for its future operation during testing. Statistical results from the theoretical findings of this study are highly relevant when compared to the actual operation of relay protection device systems. The correctness of the model calculation method proposed in this study is verified, which provides a feasible method for reliability assessment of relay protection device intelligence tests. In addition, it was also found that the failure rate of the device internal module CPU has a relatively large impact on the comprehensive availability. It is therefore recommended to focus on CPU module detection and timely replacement during the maintenance cycle of on-site operations and maintenance. Finally, the CPU module failure rate threshold for production intelligence testing was amended, which is a valuable indicator for the evolutionary optimization of quality problems in actual smart manufacturing and testing.

Evidential network‐based system reliability assessment by fusing multi‐source evidential information

AbstractExpert knowledge is an important information source for reliability assessment of those systems with limited time‐to‐failure data. For a better understanding of the degradation profiles of systems, multiple experts are oftentimes invited to express their judgments and the associated uncertainties on the reliability‐related measures of the system. Evidential variables, as an alternative of uncertainty quantification, have been extensively used in expert systems to quantify the epistemic uncertainty of the elicited expertise. When eliciting the reliability‐related information by evidential variables, experts only need to express the possible ranges of reliability‐related measures and their associated probabilities. Such a type of information well caters to the experts’ elicitation process. In this article, an evidential network (EN)‐based reliability assessment method is put forth by fusing multi‐source evidential information. The proposed method mainly contains three steps. In the first place, the multi‐source evidential information related to all components is elicited from experts in the form of evidential variables. Next, the evidential variable of the component reliability is assessed via a constrained optimization model by treating all pieces of multi‐source evidential information as constraints. The component reliability results are transformed into pieces of mass functions of the components’ states under the theory of belief functions. The system reliability‐box and the reliability‐box over time are, therefore, calculated by the EN model by inputting all mass functions of components’ states. A pipeline system and a chip cutting system are exemplified to examine the effectiveness of the proposed method.

Reliability Assessment of the Configuration of Dynamic Uninterruptible Power Sources: A Case of Data Centers

The number of data centers worldwide is increasing year by year, mostly because of the development of cloud services and applications. In the near future, the rate of construction of data centers will grow, with a corresponding increase in their electrical energy consumption. The requirements of the reliability of the electrical power supply of data centers are one of the highest among industrial power consumers, since uninterrupted power supply is critically important for the continuous functioning of server hardware. The assessment of electrical power supply reliability is one of the most important parts of the design process of data centers. However, the speed of the development of new power equipment does not always make it possible to use classical probabilistic and statistical methods for reliability assessment. Therefore, the development of new methods for reliability assessment based on alternative approaches, which can eliminate the disadvantages of probabilistic and statistical methods, are of great interest. This paper discusses the alternative method for analyzing the reliability of electrical power supply for the case of data centers. The method defines the reliability through the internal information of the system that characterizes the system’s topology, flows of information, energy, and matter in the system.

A Bayesian model averaging method for software reliability modeling and assessment

AbstractThe software reliability modeling is of great significance in improving software quality and managing the software development process. However, the existing methods are not able to accurately model software reliability improvement behavior because existing single model methods rely on restrictive assumptions and combination models cannot well deal with model uncertainties. In this article, we propose a Bayesian model averaging (BMA) method to model software reliability. First, the existing reliability modeling methods are selected as the candidate models, and the Bayesian theory is used to obtain the posterior probabilities of each reliability model. Then, the posterior probabilities are used as weights to average the candidate models. Both Markov Chain Monte Carlo (MCMC) algorithm and the Expectation–Maximization (EM) algorithm are used to evaluate a candidate model's posterior probability and for comparison purpose. The results show that the BMA method has superior performance in software reliability modeling, and the MCMC algorithm performs better than EM algorithm when they are used to estimate the parameters of BMA method.

Discrete-Time Bayesian Networks Applied to Flexible Coping Strategies of Nuclear Power Plant Systems

The Fukushima Daiichi accident prompted the nuclear community to find a new solution to reduce the risk in nuclear power plants (NPPs) due to beyond-design-basis external events (BDBEEs). An implementation guide for diverse and flexible coping strategies (FLEX) has been presented by the Nuclear Energy Institute to manage the challenges of BDBEEs and enhance reactor safety. Due to the uniqueness of the FLEX systems, these systems can potentially carry dependencies among components not commonly modeled in NPPs. In this study, we investigate the effectiveness and applicability of both Bayesian networks (BNs) and discrete-time Bayesian networks in the reliability analysis of FLEX equipment. The study compares BNs with two other reliability assessment methods: fault tree and Markov chain. These methods are also shown to be capable of mapping into BNs to perform a reliability analysis of FLEX systems. A neutral dependency algorithm is used to simplify the conditional probability tables and reduce the complexity of the BNs. The results indicate that BNs are not only a powerful method for modeling FLEX strategies but are also effective techniques for inclusion of the dynamics of FLEX equipment in probabilistic risk analysis.

A decoupled double-loop method with the adaptive allowable limits for probabilistic performance-based design optimization

In this study, inspired by the idea of the sequential optimization and reliability assessment (SORA) method, a new decoupled double-loop method for probabilistic performance-based design optimization (PPBDO) has been proposed. The highlight of the proposed method is the independence of this method from the type of problem and the limit state function; in such a way that it can be used in non-linear problems with multiple probabilistic constraints with the implicit form of the limit state function. However, it requires less computational effort than conventional double-loop methods. In each cycle of this method, deterministic performance-based design optimization was performed and then the reliability of the optimal design was evaluated. If the results of the reliability assessment are not desirable, the allowable limit of the constraints in the deterministic optimization problem will be changed based on the results of the reliability assessment. This process repeats until the results of the reliability assessment become desirable. The metaheuristic enhanced vibrating particle system algorithm and the Monte Carlo simulation method were used for optimization process and reliability analysis, respectively. To evaluate the efficiency of the proposed method, the optimization problem was defined so as to reduce the structural weight while meeting the acceptance criteria for performance-based design of steel moment frames according to ASCE 41-13. The PPBDO of three- and nine-story steel moment frames were investigated and the results showed that the proposed method was able to reduce the volume of calculations and direct the search path toward a reliable space.

A fatigue reliability assessment approach for wind turbine blades based on continuous time Bayesian network and FEA

AbstractWind turbine blades made by composite materials (CWTBs), encounter fatigue failures, such as cracks, fractures, delamination, etc. Finite Element Analysis (FEA) is applied for fatigue performance simulations of CWTBs as the full‐scale testing is costly. To consider correlated failures and uncertainties in load and material parameters, this paper proposes a fatigue reliability assessment method based on continuous time Bayesian network and FEA. Specifically, the dangerous regions of each component of CWTBs are determined by finite element fatigue simulation. The failure probability distributions of components are then computed by quantifying the uncertainties of several factors including the load and material parameters. A continuous time Bayesian network model is constructed for the fatigue reliability of CWTBs. The performance of the proposed method is verified by a comprehensive analysis with the results of discrete time Bayesian networks.

Underground Sedimentary Rock Moisture Permeation Damage Assessment Based on AE Mutual Information

Moisture permeation would lead to damage accidents. It is of great significance to measure and monitor the moisture states of underground sedimentary rock engineering in real time. In this research, a reliability assessment method for underground rock based on acoustic emission (AE) mutual information was proposed. First, the mutual relationship between multiple AE parameters and moisture permeation damage was analyzed. Then the mechanical parameters were fit by the probabilistic method, so that moisture permeation damage could be assessed with a data-driven approach. The experiment showed that this method could identify samples with different moisture permeation damage levels. The moisture permeation damage could be predicted in the early stage. Moreover, this method could reduce sensor error through probabilistic analysis.

Reliability Assessment Method for Power Grid Security and Stability Control Devices Based on Weibull Fault Rate Fitting

Reliability Assessment Method for Power Grid Security and Stability Control Devices Based on Weibull Fault Rate Fitting

Human Reliability Analysis on Fresh Fruit Bunches Sorting Workers

Demand for Crude Palm Oil (CPO) at PT. Wijaya Borneo Tiganna for this year has increased. However, the increase in production was not in line with human error of 74% in the Fresh Fruit Bunches (FFB) Sorting Department from the 80% minimum percentage desired by the company. The purpose of this study was to analyze the reliability value of sorting workers. The method used is the Human Reliability Assessment Method seen from related tasks and context related to identify Human Error Probability (HEP) with a fault tree to provide recommendations for reducing human error. There are 3 types of rework in the TBS Sorting Section. The HEP using the HEART method is 1.1103148 while the CREAM method is 0.10608. The reliability of workers in carrying out tasks can still be improved with skills and accuracy in the sorting and grading of FFB.

Thermal reliability assessment and sensitivity analysis of 18,650 cylindrical lithium-ion battery

In order to solve the problems of thermal safety and thermal reliability of the battery, a method of thermal reliability assessment and reliability sensitivity analysis for an 18,650 cylindrical lithium-ion battery is proposed. The finite element analysis (FEA) software is used to establish an electrochemical-thermal coupling model of the lithium-ion battery. By comparing the expected and experimental results, the accuracy of the proposed model is verified, and the model is subsequently used to analyze the thermal reliability. The adaptive Kriging method is used to establish the thermal reliability performance function model and assess the thermal reliability of the lithium-ion battery. Additionally, in order to study the influence of the material parameters on the thermal reliability of the battery, gradual reliability and reliability sensitivity analyses are conducted. The results show that when the density is 3150 kg/m3, the reliability reaches 0.993, which is 14.4 % higher than that of the existing battery, while the sensitivity value decreases. When the specific heat capacity is 1310 J/kg·K, the reliability of the battery reaches 0.986, which is 13.6 % higher than that of the existing battery, while the sensitivity value decreases. Lastly, the thermal conductivity has little influence on the thermal reliability of the battery.

Building Fire Safety Management Analysis Based On Building Safety System Reliability Assessment Method In Green House Display Building – Cibinong National Research And Innovation Agency, Bogor Regency

Fires should be alerted and anticipated or detected as early as possible. If a fire occurs, it will be fatal such as damage to buildings, infrastructure, To prevent or anticipate fires, detection must be carried out as early as possible and prepared facilities or infrastructure for fire safety. The Green House display building is an exhibition building of living plants from various mountains in Indonesia including the mountains of Java, the mountains of Sumatra, the mountains of Kalimantan, and the mountains of Papua. Plants and soil are taken directly from the associated location. This building is allegedly very prone to fire because there are so many flammable materials such as leaves, wood and others. As well as forests in Indonesia, especially in Kalimantan and Sumatra where fires often occur. Therefore, the application of Building Fire Safety Management in this building must be applied as well as possible. Active protection systems such as fire detection devices for this building cannot use ordinary systems, because in this building, the atmosphere is like mountains with many big trees. The inspection can be carried out by several methods, including analyzing based on applicable standards and analyzing based on the assessment of the reliability of the building safety system referring to the Settlement Research and Development Center, Research and Development Agency PU, Department of Public Works in 2005 (Pd-T-11-2005-C).

Transformer maintenance decision based on condition monitoring and fuzzy probability hybrid reliability assessment

Equipment maintenance decision is a very critical technology in power grid asset management. The traditional maintenance decision focuses on the analysis of the operation performance of the equipment itself, but lacks the analysis and description of the fuzziness of power equipment outage parameters. Therefore, this paper establishes the reliability assessment model based on equipment health condition monitoring, which makes the maintenance decision transition from equipment level to system level, and the fuzzy mathematics theory is introduced to establish a model describing the fuzziness of equipment failure rate parameter to make system reliability assessment and equipment maintenance decision scheme. Firstly, this paper proposes a fuzzy failure rate model of power transformer based on condition monitoring. Then the fuzzy parameters are combined with the conventional probabilistic reliability assessment method to establish the fuzzy probability hybrid reliability assessment model of the transmission system, and the fuzzy maintenance reliability benefit index is defined and deduced. Finally, a maintenance strategy of transformer based on fuzzy probability hybrid reliability assessment and fuzzy set theory is proposed, and the case study is carried out on a regional power grid in East China. The results show that the proposed model provides an improved maintenance strategy of power equipment.

Reliability assessment of transformer insulating oil using accelerated life testing

To improve the reliability and reduce the maintenance cost of transformer oil, a life prediction of transformer oil is needed so that the maintenance of transformer can be performed correctly. However, it is difficult to predict the reliable lifetime of transformer oil accurately because of its unkind operating condition at different environments. To solve this problem, based on the theory of accelerated life testing (ALT), a reliability assessment method for transformer insulating oil on a normal operational conditions is proposed. An inverse power Weibull distribution model for insulating oil lifetime with voltage is built. Numerical procedure of model parameter estimation is presented, the variances of model parameters and reliability indices, which including mean lifetime, reliability, reliable lifetime at given reliability and failure rate, are derived. The feasibility and correctness of the proposed method are validated by real lifetime data of transformer insulating oil in literature. The reliability of transformer insulating oil used at normal usage conditions are predicted by the proposed method, and the point and interval estimations of reliability indices are evaluated. The results show that reliable lifetime and mean lifetime under reliability limit should be considered simultaneously in repair or replacement of transformer insulating oil.

An efficient and effective method for reliability assessment of project duration

ABSTRACT The reliability assessment of the project duration is essential for successful project management; this has prompted the development of various methods to assess whether a project will be completed within the target duration. In this study, an efficient and effective method for assessing the reliability of project duration based on the method of moments is proposed, which utilizes bivariate-dimension reduction technology and the third-moment reliability index. The proposed method can avoid the extensive calculations of various factors in the commonly used methods: the correlation coefficients between each pair of paths and the joint failure probability of each pair of representative paths. From two numerical examples, it can be concluded that the proposed method can obtain almost the same results as Monte Carlo simulations with less calculation.