Catalogue Search | MBRL
Are you sure you want to remove the book from the shelf?
{{itemTitle}}
22,916
result(s) for
"Reliability evaluation"
Sort by:
The foundations of operational resilience - assessing the ability to operate in an anti-access/area denial (A2/AD) environment : the analytical framework, lexicon, and characteristics of the Operational Resilience Analysis Model (ORAM)
\"Although much work has been done considering the issue of airbase resilience especially in the Asia-Pacific region these studies have typically focused on a single aspect of the problem (such as hardening or runway repair) but have not considered the issues in total. There is a need to view the issue more holistically, especially given the strategic implications of U.S. power projection in anti-access/area denial (A2/AD) environments. The authors of this report developed a modeling framework and lexicon for conducting a detailed analysis of future Air Force operational resilience in an A2/AD environment; the analysis itself focused on different regions (Pacific, Southwest Asia, etc.) to bound the problem and identify a robust set of strategic assumptions and planning requirements. The study was set within the context of efforts to rebalance the joint force in the Asia-Pacific region. This report describes the Operational Resilience Analysis Model (ORAM) built for this effort, which was used to evaluate the impact of different courses of action from an operational standpoint. The authors explain the ORAM model, discuss the inputs that go into modeling Blue (friendly) and Red (enemy) capabilities, and illustrate the model using a simple notional case. They conclude with some suggestions for follow-on work to improve the functionality of ORAM and to address data uncertainties in the model\"--Publisher's website.
Book
A comparative study for adaptive surrogate-model-based reliability evaluation method of automobile components
PurposeThis study conducts a comparative study on the performance of reliability assessment methods based on adaptive surrogate models to accurately assess the reliability of automobile components, which is critical to the safe operation of vehicles.Design/methodology/approachIn this study, different adaptive learning strategies and surrogate models are combined to study their performance in reliability assessment of automobile components.FindingsBy comparing the reliability evaluation problems of four automobile components, the Kriging model and Polynomial Chaos-Kriging (PCK) have better robustness. Considering the trade-off between accuracy and efficiency, PCK is optimal. The Constrained Min-Max (CMM) learning function only depends on sample information, so it is suitable for most surrogate models. In the four calculation examples, the performance of the combination of CMM and PCK is relatively good. Thus, it is recommended for reliability evaluation problems of automobile components.Originality/valueAlthough a lot of research has been conducted on adaptive surrogate-model-based reliability evaluation method, there are still relatively few studies on the comprehensive application of this method to the reliability evaluation of automobile component. In this study, different adaptive learning strategies and surrogate models are combined to study their performance in reliability assessment of automobile components. Specially, a superior surrogate-model-based reliability evaluation method combination is illustrated in this study, which is instructive for adaptive surrogate-model-based reliability analysis in the reliability evaluation problem of automobile components.
Journal Article
A Cloud Model-Based Optimal Combined Weighting Framework for the Comprehensive Reliability Evaluation of Power Systems with High Penetration of Renewable Energies
Reliability has long been a critical attribute of power systems that cannot be ignored. Numerous blackout events have highlighted the increasing risk of outages in power systems due to the prominence of high-proportion power electronics and renewable energy utilization. Traditional reliability assessment methods, which typically take dozens of hours to assess the adequacy of steady-state conditions, cannot reflect the real-time reliability performance of the system. Moreover, the weakness identification methods can only quantify the impact of component outages while ignoring other important operational factors. To address these issues, this paper constructs a three-hierarchy reliability evaluation index system (REIS) for power systems, consisting of the comprehensive reliability evaluation index (CREI) as the top hierarchy, four primary indices in the middle, and lots of subjective and objective indices on the bottom. To quantify the performance of different calculation methods for these indices, a combined weighting framework is proposed. Finally, the REIS level is evaluated according to the Wasserstein distances between the CREI cloud model and standard cloud models. In the case study, the proposed method is verified through its application to the power grids of two cities in a province in southern China, demonstrating its practicality and effectiveness.
Journal Article
Analytical Reliability Evaluation Framework of Three-Dimensional Engineering Slopes
An analytical three-dimensional slope reliability evaluation framework was developed in this work independent of use of numerical simulations. The slope stability analysis was necessarily carried out by utilizing an extended three-dimensional Morgenstern–Price method, which was characterized by analytical formulations and competitive computational efficiency. Incorporation of the presented stability analysis method into response surface methodology led to an effective slope reliability evaluation framework. The applicability and superiority of this framework was examined and validated using a real complicated landslide case reported in practice, and a hypothetical slope example widely adopted in the literature. The impact of correlation coefficients and probability distribution patterns on the slope reliability assessment results was further addressed to derive additional benefits of this framework.
Journal Article
Reliability evaluation and big data analytics architecture for a stochastic flow network with time attribute
A network with multi-state (stochastic) elements (arcs or nodes) is commonly called a stochastic flow network. It is important to measure the system reliability of a stochastic flow network from the perspective of operations management. In the real world, the system reliability of a stochastic flow network can vary over time. Hence, a critical issue emerges—characterizing the time attribute in a stochastic flow network. To solve this issue, this study bridges (classical) reliability theory and the reliability of a stochastic flow network. This study utilizes Weibull distribution as a possible reliability function to quantify the time attribute in a stochastic flow network. For more general cases, the proposed model and algorithm can apply any reliability function and is not limited to Weibull distribution. First, the reliability of every single component is modeled by Weibull distribution to consider the time attribute, where such components comprise a multi-state element. Once the time constraint is given, the capacity probability distribution of elements can be derived. Second, an algorithm to generate minimal component vectors for given demand is provided. Finally, the system reliability can be calculated in terms of the derived capacity probability distribution and the generated minimal component vectors. In addition, a big data architecture is proposed for the model to collect and estimate the parameters of the reliability function. For future research in which very large volumes of data may be collected, the proposed model and architecture can be applied to time-dependent monitoring.
Journal Article
A Digital Twin-Driven Life Prediction Method of Lithium-Ion Batteries Based on Adaptive Model Evolution
Accurate life prediction and reliability evaluation of lithium-ion batteries are of great significance for predictive maintenance. In the whole life cycle of a battery, the accurate description of the dynamic and stochastic characteristics of life has always been a key problem. In this paper, the concept of the digital twin is introduced, and a digital twin for reliability based on remaining useful cycle life prediction is proposed for lithium-ion batteries. The capacity degradation model, stochastic degradation model, life prediction, and reliability evaluation model are established to describe the randomness of battery degradation and the dispersion of the life of multiple cells. Based on the Bayesian algorithm, an adaptive evolution method for the model of the digital twin is proposed to improve prediction accuracy, followed by experimental verification. Finally, the life prediction, reliability evaluation, and predictive maintenance of the battery based on the digital twin are implemented. The results show the digital twin for reliability has good accuracy in the whole life cycle. The error can be controlled at about 5% with the adaptive evolution algorithm. For battery L1 and L6 in this case, predictive maintenance costs are expected to decrease by 62.0% and 52.5%, respectively.
Journal Article
A unified reliability evaluation framework for aircraft turbine rotor considering multi-site failure correlation
Multiple dangerous sites often coexist in complex structures like aircraft turbine rotors, and its failure-correlated reliability evaluation usually occurs the thorny problems of high-efficacy computing and correlation quantification. In this paper, a novel unified reliability evaluation framework is proposed: to meet the high-efficacy computing demand of multi-site reliability evaluation, a new optimized Kriging surrogate-based improved importance sampling (OKS-IIS) method is first presented; furthermore, to quantify the failure correlation relationships among multiple dangerous sites, a novel failure correlation analysis (FCA) strategy is further developed. A typical reliability evaluation of high-pressure turbine rotor with multiple dangerous sites is selected, to validate the effectiveness of the proposed framework. Methods comparison show that the OKS-IIS method can improve computing efficiency while keeping computing accuracy, and the FCA strategy can accurately quantify the multi-site failure correlation. The current efforts would shed a light on the complex reliability evaluation problems involving failure correlation.
Journal Article
Reliability evaluation of a multi-state air transportation network meeting multiple travel demands
In last decades, air transportation plays an important role in global economy. Several scholars have studied optimizing air transportation system or proposed reliability evaluation algorithms from airline management viewpoints. This work evaluates the reliability of an air transportation system from the perspective of travel agency instead. An air transportation system can be modeled as a multi-state air transportation network (MATN) wherein each node represents an airport and each arc denotes a flight carrying passengers between a pair of airports from scheduled departure time to scheduled arrival time. Significantly, this study focuses on investigating the reliability of multiple travel demands. Therefore, the reliability of an MATN is defined as the probability that a set of demands can be carried successfully under constraints of time and number of stopovers. This study employs the concept of minimal paths in reliability evaluation. Subsequently, a searching procedure is added to the proposed algorithm. In addition, an illustrative example and a case study are utilized to demonstrate the proposed algorithm and discuss the implications of reliability evaluation for the management of travel agency.
Journal Article
Evaluation of the nutrition literacy assessment questionnaire for college students and identification of the influencing factors of their nutrition literacy
Background
Nutrition health has become a major public health issue in both high and middle-income countries. Nutrition literacy is an important indicator to evaluate the effect of public health intervention and one of the important concepts in health promotion. Thus, this study aimed to verify the reliability and validity of a nutrition literacy assessment questionnaire (NLAQ) and investigate the associated factors of nutrition literacy among college students.
Methods
We conducted a cross-sectional online survey of college students from April to November 2022 in Wuhan (
N
= 774). We employed the Cronbach’s alpha coefficient, exploratory and confirmatory factor analysis to evaluate the reliability and validity. We used latent profile analysis to classify the nutrition literacy. We conducted Chi-square test and binary logistic regression to identify the influencing factors.
Results
The Cronbach’s alpha coefficient of the NLAQ and its dimension was ranging from 0.837 to 0.909. The common factors were consistent with the original dimensions. All indicators met the requirements (χ
2
/df = 6.16 < 8, GFI = 0.929, NFI = 0.939, CFI = 0.948, RMSEA = 0.082 < 0.1). College students’ disciplines (
χ
2
= 7.769,
P
= 0.021), mothers’ education level (
χ
2
= 26.599,
P
< 0.001), and fathers’ occupation type (
χ
2
= 11.218,
P
= 0.024) had impacts on nutrition literacy.
Conclusion
The NLAQ has good reliability and validity, and could be used as a measurement tool to evaluate college students’ nutrition literacy. Schools and families should take targeted measures to improve the college students’ nutrition literacy.
Journal Article
Assessment of Dependent Performance Shaping Factors in SPAR-H Based on Pearson Correlation Coefficient
With the improvement of equipment reliability, human factors have become the most uncertain part in the system. The standardized Plant Analysis of Risk-Human Reliability Analysis (SPAR-H) method is a reliable method in the field of human reliability analysis (HRA) to evaluate human reliability and assess risk in large complex systems. However, the classical SPAR-H method does not consider the dependencies among performance shaping factors (PSFs), which may cause overestimation or underestimation of the risk of the actual situation. To address this issue, this paper proposes a new method to deal with the dependencies among PSFs in SPAR-H based on the Pearson correlation coefficient. First, the dependence between every two PSFs is measured by the Pearson correlation coefficient. Second, the weights of the PSFs are obtained by considering the total dependence degree. Finally, PSFs’ multipliers are modified based on the weights of corresponding PSFs, and then used in the calculating of human error probability (HEP). A case study is used to illustrate the procedure and effectiveness of the proposed method.
Journal Article