A novel tool for Bayesian reliability analysis using AHP as a framework for prior elicitation

Quantitative risk assessment (QRA) is a powerful and popular technique to support risk-based decisions. Unfortunately, QRAs are often hampered by significant uncertainty in the frequency of failure estimation for physical assets. This uncertainty is largely due to lack of quality failure data in published sources. The failure data may be limited, incompatible and/or outdated. Consequently, there is a need for robust methods and tools that can incorporate all available information to facilitate reliability analysis of critical assets such as pipelines, pressure vessels, rotating equipment, etc. This paper presents a novel practical approach that can be used to help overcome data scarcity issues in reliability analysis. A Bayesian framework is implemented to cohesively integrate objective data with expert opinion with the aim toward deriving time to failure distributions for physical assets. The Analytic Hierarchy Process is utilized to aggregate time to failure estimates from multiple experts to minimize biases and address inconsistencies in their estimates. These estimates are summarized in the form of informative priors that are implemented in a Bayesian update procedure for the Weibull distribution. The flexibility of the proposed methodology allows for efficiently dealing with data limitations. Application of the proposed approach is illustrated using a case study.     

Model-based information fusion investigation on fault isolation of subsea systems considering the interaction among subsystems and sensors

The offshore oil industry has expanded to deep water and Arctic. The harsh operating conditions (e.g., ice and strong wind) and increasing complicated system raise the occurrence likelihood of system faults. This requires timely fault isolation and management in the subsea system. However, the offshore oil industry mainly relies on humans to isolate faults based on alarms. With harsh operating conditions and increasing complicated system, this industry urgently needs research on more efficient fault isolation and cause diagnosis methods. Unfortunately, limited research is conducted on fault isolation method in the offshore oil industry. Furthermore, in industry 4.0 era, large amounts of information are obtained. This provides precondition for the application of information fusion technique which aims to improve diagnosis results. However, to the authors’ knowledge, information fusion has not been much studied in the fault isolation of the offshore oil industry. Moreover, the interaction of different subsystems contains valuable information. How the interaction of different subsystems can influence the fault diagnosis has not been explored. This paper proposes a Bayesian network (BN) based method for timely fault isolation and cause diagnosis for the offshore oil industry. The work fuses different information, and it also includes the dependency among different subsystems in the fault diagnosis. As an important alarm source, false alarms are also taken into account in the model. A case study on the subject of the subsea wellhead and chemical injection systems is conducted to demonstrate the functions and merits of the proposed method.     

Analysis of Extreme Summer Rainfall Using Climate Teleconnections and Typhoon Characteristics in South Korea1

Abstract: It is now widely acknowledged that climate variability modulates the frequency of extreme hydrological events. Traditional methodologies for hydrologic frequency analysis are not devised to account for variation in the exogenous teleconnections. Flood frequency analysis is further plagued by the assumptions of stationary in the causal structure as well as ergodicity. Here, we propose a dynamical hierarchical Bayesian analysis to account for exogenous forcing that govern the summer season rainfall. The precursors for Korean summer rainfall at different frequencies are identified utilizing wavelet and independent component analyses. The sea surface temperatures, the ensemble of rainfall predictions by General Circulation Model, in addition to the typhoon attributes were found to have direct correlation with extreme rainfall events and were used as inputs to the logistic regression model. The model parameters are estimated using Markov Chain Monte Carlo and the resulting posterior distributions associated with individual inputs are analyzed to advance our understanding of the spatiotemporal impact of the teleconnections. Eight rainfall stations throughout Korea are considered in this analysis. We demonstrate that the probability of occurrence of extreme events could be successfully projected at a 90% rate of correct classification of extreme events.     

融合多源时空数据的地下水硫酸盐预测模型

准确预测地下水SO₄ ^2-空间变化趋势对改善地下水质量、提高区域地下水管理水平具有重要意义.以2011、2014、2017和2020年叶尔羌河流域平原区土地覆盖数据、土壤参数数据、数字高程数据等多源时空数据和地下水pH值为特征变量,分析其与地下水SO₄ ^2-浓度的相关性,利用贝叶斯优化算法优化随机森林回归,建立BOA-RFR模型,并基于BOA-RFR模型对特征变量进行重要性分析,对模型预测精度进行评价,最后生成地下水SO₄ ^2-预测图.结果表明,pH值、地面高程（GE）和贡献区荒地（BAR）面积占比作为影响地下水水化学组分的重要参数,与地下水SO₄ ^2-浓度均呈现极显著负相关,对地下水SO₄ ^2-浓度预测的重要度均大于25 %;地统计插值方法作为空间分布预测建模的辅助手段,加入辅助样本后的BOA-RFR模型,地下水SO₄ ^2-浓度预测的R²均大于0.96,且多辅助样本构建模型的RMSE和MAE最大值较少样本模型的最小值分别降低了4.7 %和23.8 %;在地下水SO₄ ^2-浓度预测中,高SO₄ ^2-地下水向叶尔羌河流域平原区东北部富集,且面积呈扩张趋势.     

基于LS-SVM的供水管网安全性评价方法研究

为评价城市供水管网的安全性,保障其正常运行,笔者基于多元分类最小二乘支持向量机(LS-SVM)的方法,在对城市供水管网安全运行影响因素总结与分析的基础上,构建供水管网安全性评价的指标因素集与评价模型,通过对有限的经验数据的学习,建立供水管网安全性与其影响因素之间的非线性关系。运用该模型进行实例仿真模拟,通过与实际安全等级及BP神经网络模型预测安全等级之间的对比表明:基于LS-SVM的供水管网安全性评价方法具有较高的精度,正确分类率可以达到83.33%。     

基于相空间神经网络耦合模型的径流降尺度分析

基于混沌理论和神经网络理论,研究水文科学的尺度问题,将混沌神经网络分析方法应用于径流的降尺度分析。首先通过对年径流量分解到月径流量的分解系数的分析,证明了分解系数具有混沌特性;其次利用相空间BP神经网络模型对分解系数进行预测,并根据预测结果进行月径流量的降尺度计算。实例研究表明,用神经网络拟合分解系数相空间的相点演化非线性关系和用相空间神经网络模型对径流作降尺度分析是可行的。     

水市场双边叫价贝叶斯博弈模型及机制设计研究

通过水权交易提高水资源的使用效率和配置效率,实现水资源最优配置。水权和水市场理论研究是水资源可持续利用的重要研究课题,而水权交易模型是水市场理论与实践中水权交易的核心。针对水权交易市场最常见的双边交易,采用双边拍卖的交易模式,通过对水权交易市场的双边交易进行描述分析,建立了水权交易买卖双方叫价拍卖的不完全信息博弈模型,给出双方叫价拍卖的机制设计。该机制具有有效性和激励相容性,最后给出算例。     

生命线工程网络系统抗震可靠性分析方法综述

生命线工程系统地震反应分析的一个重要任务是计算系统的抗震可靠度,因此,寻找一种高效合适的系统可靠性分析方法至关重要.对生命线工程网络系统抗震可靠性分析中目前常用的几种算法进行了系统的比较、分析和综述.     

Using a State-and-Transition Approach to Manage Endangered Eucalyptus albens (White Box) Woodlands

Eucalyptus albens (White Box) woodlands are among the most poorly conserved and threatened communities in Australia. Remnants are under further threat from stock grazing, deteriorating soil conditions, weed invasion, and salinity. There is an urgent need to restore degraded White Box and other woodland ecosystems to improve landscape function. However, there is still a poor understanding of the ecology of degraded woodland ecosystems in fragmented agricultural landscapes, and consequently a lack of precise scientific guidelines to manage these ecosystems in a conservation context. State and Transition Models (STMs) have received a great deal of attention, mainly in rangeland applications, as a suitable framework for understanding the ecology of complex ecosystems and to guide management. We have developed a STM for endangered White Box woodlands and discuss the merits of using this approach for land managers of other endangered ecosystems. An STM approach provides a greater understanding of the range of states, transitions, and thresholds possible in an ecosystem, and provides a summary of processes driving the system. Importantly, our proposed STM could be used to clarify the level of “intactness” of degraded White Box woodland sites, and provide the impetus to manage different states in complementary ways, rather than attempting to restore ecosystems to one pristine stable state. We suggest that this approach has considerable potential to integrate researcher and land manager knowledge, focus future experimental studies, and ultimately serve as a decision support tool in setting realistic and achievable conservation and restoration goals.     

MODELING TRANSIENT pH DEPRESSIONS IN COASTAL STREAMS OF BRITISH COLUMBIA USING NEURAL NETWORKS1

ABSTRACT: Transient events in water chemistry in small coastal watersheds, particularly pH depressions, are largely driven by inputs of precipitation. While the response of each watershed depends upon both the nature of the precipitation event and the season of the year, how the response changes over time can provide insight into landscape changes. Neural network models for an urban watershed and a rural‐suburban watershed were developed in an attempt to detect changes in system response resulting from changes in the landscape. Separate models for describing pH depressions for wet season and dry season conditions were developed for a seven year period at each watershed. The neural network models allowed separation of the effects of precipitation variations and changes in watershed response. The ability to detect trends in pH depression magnitudes was improved by analyzing neural network residuals rather than the raw data. Examination of sensitivity plots of the models indicated how the neural networks were affected by different inputs. There were large differences in effects between seasons in the rural‐suburban watershed whereas effects in the urban watershed were consistent between seasons. During the study period, the urban watershed showed no change in pH depression response, while the rural‐suburban watershed showed a significant increase in the magnitude of pH depressions, likely the result of increased urbanization.