Reliability Engineering & System Safety最新文献

{"title":"On fractional moment estimation from polynomial chaos expansion","authors":"Lukáš Novák ,&nbsp;Marcos Valdebenito ,&nbsp;Matthias Faes","doi":"10.1016/j.ress.2024.110594","DOIUrl":"10.1016/j.ress.2024.110594","url":null,"abstract":"<div><div>Fractional statistical moments are utilized for various tasks of uncertainty quantification, including the estimation of probability distributions. However, an estimation of fractional statistical moments of costly mathematical models by statistical sampling is challenging since it is typically not possible to create a large experimental design due to limitations in computing capacity. This paper presents a novel approach for the analytical estimation of fractional moments, directly from polynomial chaos expansions. Specifically, the first four statistical moments obtained from the deterministic coefficients of polynomial chaos expansion are used for an estimation of arbitrary fractional moments via Hölder’s inequality. The proposed approach is utilized for an estimation of statistical moments and probability distributions in four numerical examples of increasing complexity. Obtained results show that the proposed approach achieves a superior performance in estimating the distribution of the response, in comparison to a standard Latin hypercube sampling in the presented examples.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"254 ","pages":"Article 110594"},"PeriodicalIF":9.4,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142561388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Maximum entropy-based modeling of community-level hazard responses for civil infrastructures","authors":"Xiaolei Chu,&nbsp;Ziqi Wang","doi":"10.1016/j.ress.2024.110589","DOIUrl":"10.1016/j.ress.2024.110589","url":null,"abstract":"<div><div>Perturbed by natural hazards, community-level infrastructure networks operate like many-body systems, with behaviors emerging from coupling individual component dynamics with group correlations and interactions. It follows that we can borrow methods from statistical physics to study the response of infrastructure systems to natural disasters. This study aims to construct a joint probability distribution model to describe the post-hazard state of infrastructure networks and propose an efficient surrogate model of the joint distribution for large-scale systems. Specifically, we present maximum entropy modeling of the regional impact of natural hazards on civil infrastructures. Provided with the current state of knowledge, the principle of maximum entropy yields the “most unbiased“ joint distribution model for the performances of infrastructures. In the general form, the model can handle multivariate performance states and higher-order correlations. In a particular yet typical scenario of binary performance state variables with knowledge of their mean and pairwise correlation, the joint distribution reduces to the Ising model in statistical physics. In this context, we propose using a dichotomized Gaussian model as an efficient surrogate for the maximum entropy model, facilitating the application to large systems. Using the proposed method, we investigate the seismic collective behavior of a large-scale road network (with 8,694 nodes and 26,964 links) in San Francisco, showcasing the non-trivial collective behaviors of infrastructure systems.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"254 ","pages":"Article 110589"},"PeriodicalIF":9.4,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142527385","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigation of essential parameters for the design of offshore wind turbine based on structural reliability","authors":"Fucheng Han ,&nbsp;Wenhua Wang ,&nbsp;Xiao-Wei Zheng ,&nbsp;Xu Han ,&nbsp;Wei Shi ,&nbsp;Xin Li","doi":"10.1016/j.ress.2024.110601","DOIUrl":"10.1016/j.ress.2024.110601","url":null,"abstract":"<div><div>The probabilistic-based design method is gradually gaining attention in the wind industry because it provides more accurate modeling of uncertainty variables than that of traditional methods. Unfortunately, the numerous uncertainty variables involved in structural design are major obstacles to the successful application of this method. Therefore, this study presents a sensitivity analysis (SA) of a benchmark monopile offshore wind turbine (OWT) to screen the top-ranking variables from the viewpoint of reliability. Primarily, a comprehensive reliability SA framework of OWT is proposed, in which a novel measurement of soil uncertainties is conducted using quantitative analysis from the perspective of soil structure interaction (SSI). Subsequently, a reliability SA is conducted to explore the crucial variables influencing the structural safety from the uncertain clusters. The results indicate that Young's modulus, structural geometry, and SSI have significant effects on the structural reliability of excessive vibration failure. The hydrodynamic and aerodynamic load variables exhibit the most prominent influence on excessive deflection failure. Additionally, the SSI uncertainties exhibit a non-negligible effect in affecting the structural reliability, i.e., the lateral bending stiffness shows more sensitivity to the normal operation cases, whereas the impact of joint stiffness is more remarkable in parked scenarios.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"254 ","pages":"Article 110601"},"PeriodicalIF":9.4,"publicationDate":"2024-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142554004","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Damage-driven framework for reliability assessment of steam turbine rotors operating under flexible conditions","authors":"Hang-Hang Gu ,&nbsp;Run-Zi Wang ,&nbsp;Kun Zhang ,&nbsp;Kai-Shang Li ,&nbsp;Li Sun ,&nbsp;Xian-Cheng Zhang ,&nbsp;Shan-Tung Tu","doi":"10.1016/j.ress.2024.110578","DOIUrl":"10.1016/j.ress.2024.110578","url":null,"abstract":"<div><div>The high-temperature rotating structures (HTRS), e.g., steam turbine rotors, often operate in extremely harsh environments with a flexible load condition during peak shaving of power system. In this work, a damage-driven framework for reliability assessment is developed in terms of the cumulative damage-damage threshold interference (CD-DT) principle, in which the cumulative damage and damage threshold are regarded as two random parameters to address uncertainties. The CD-DT principle is founded on the engineering damage theory and incorporates physics-of-failure into the probabilistic modeling of high-temperature structural reliability. Probabilistic damage analysis, correlation analysis of weak sites, system-level reliability analysis, and sensitivity analysis have been encompassed in this framework. Three numerical examples are used to verify the effectiveness and applicability of the proposed framework. Application to steam turbine rotor involving multiple weak sites with multi-damage modes illustrate the implementation procedures of the framework. Results show that the reliability-based design life of rotor decreases with the increases of start-stop frequency, the implementation of a two-shift operation would pose a threat to meeting the safety requirement of a 30-year design life. Furthermore, sensitivity analysis highlights the critical influences of initial rotor temperature and speed rising rate on rotor reliability, providing insights for operational maintenance and reliability optimization.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"254 ","pages":"Article 110578"},"PeriodicalIF":9.4,"publicationDate":"2024-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142578695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reliability-constrained configuration optimization for integrated power and natural gas energy systems: A stochastic approach","authors":"Mostafa Shabanian-Poodeh ,&nbsp;Rahmat-Allah Hooshmand ,&nbsp;Miadreza Shafie-khah","doi":"10.1016/j.ress.2024.110600","DOIUrl":"10.1016/j.ress.2024.110600","url":null,"abstract":"<div><div>With the escalating dependence on electricity and natural gas infrastructure, ensuring both reliability and economic efficiency becomes paramount. It necessitates reliability centric measures to mitigate disruptions that could cascade between these interconnected systems. To address this challenges, this paper introduces a reliability-constrained two-stage stochastic model to optimize power-to-gas (P2 G) and gas-to-power (G2P) unit placement and sizing, aiming to enhance the reliability of both systems under stochastic scenarios. The proposed model, employing Sequential Monte Carlo (SMC) within its optimization framework, seeks to minimize investment, operation, and reliability costs. By addressing temporal uncertainties in component outages for both systems and considering uncertainties in power and gas system loads with a high temporal resolution and annual load growth, the model provides a comprehensive reliability perspective. Furthermore, sensitivity analysis is conducted to explore the impact of varying Values of Lost Load (VOLL) on the planning results. Numerical evaluation, using two integrated energy systems including IEEE 14-bus-10-gas node, and large-scale energy systems including IEEE 118-bus-85-gas node integrated power-gas system (IPGS), demonstrates a significant 12.53 % improvement in overall system reliability. Furthermore, a 2.81 % reduction in operation costs and a substantial 26.3 % reduction in reliability costs, validating the effectiveness of the proposed model.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"254 ","pages":"Article 110600"},"PeriodicalIF":9.4,"publicationDate":"2024-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142527386","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A human reliability analysis method based on STPA-IDAC and BN-SLIM for driver take-over in Level 3 automated driving","authors":"Wenyi Liao ,&nbsp;Yidan Qiao ,&nbsp;Tongxin Dong ,&nbsp;Zhiming Gou ,&nbsp;Dengkai Chen","doi":"10.1016/j.ress.2024.110577","DOIUrl":"10.1016/j.ress.2024.110577","url":null,"abstract":"<div><div>Human factors play an important role in the take-over process of Level 3 (L3) automated driving. This paper combines Systems Theoretic Process Analysis (STPA) and Information, Decision and Action in Crew context (IDAC) for qualitative analysis and Bayesian Network (BN) and Success Likelihood Index Method (SLIM) for quantitative calculation to obtain the main performance shaping factors (PSFs) and evaluation indicators that cause human errors. Firstly, the STPA-IDAC method is used to analyze unsafe control actions (UCAs) for take-over process and form the mapping relationship of UCAs-IDA-PSFs. Secondly, the BN of human reliability analysis for take-over process is constructed based on the BN-SLIM method. Uncertainty in rates of PSFs and evaluation indicators is addressed in a probabilistic manner using expert opinions and empirical data. After diagnostic reasoning of BN, mean variation is used to identify the main PSFs and evaluation indicators. This method can effectively identify the main PSFs and evaluation indicators that cause human errors, facilitate risk assessment and management, and reduce the human error probability (HEP).</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"254 ","pages":"Article 110577"},"PeriodicalIF":9.4,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142527383","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Self-adaptive fault diagnosis for unseen working conditions based on digital twins and domain generalization","authors":"Mehdi Saman Azari ,&nbsp;Stefania Santini ,&nbsp;Farid Edrisi ,&nbsp;Francesco Flammini","doi":"10.1016/j.ress.2024.110560","DOIUrl":"10.1016/j.ress.2024.110560","url":null,"abstract":"<div><div>In recent years, intelligent fault diagnosis based on domain adaptation has been used to address domain shifts in cyber–physical systems; however, the need for acquiring target data sufficiently limits their applicability to unseen working conditions. To overcome such limitations, domain generalization techniques have been introduced to enhance the capacity of fault diagnostic models to operate under unseen working conditions. Nevertheless, existing approaches assume access to extensive labeled training data from various source domains, posing challenges in real-world engineering scenarios due to resource constraints. Moreover, the absence of a mechanism for updating diagnostic models over time calls for the exploration of self-adaptive generalized diagnosis models that are capable of autonomous reconfiguration in response to new unseen working conditions. In such a context, this paper proposes a self-adaptive fault diagnosis system that combines several paradigms, namely Monitor-Analyze-Plan-Execute over a shared Knowledge (MAPE-K), Domain Generalization Network Models (DGNMs), and Digital Twins (DT). The MAPE-K loop enables run-time adaptation to dynamic industrial environments without human intervention. To address the scarcity of labeled training data, digital twins are used to generate supplementary data and continuously tune parameters to reflect the dynamics of new unseen working conditions. DGNM incorporates adversarial learning and a domain-based discrepancy metric to enhance feature diversity and generalization. The introduction of multi-domain data augmentation enhances feature diversity and facilitates learning correlations among multiple domains, ultimately improving the generalization of feature representations. The proposed fault diagnosis system has been evaluated on three publicly available rotating machinery datasets to demonstrate its higher performance in cross-work operation and cross-machine tasks compared to other state-of-the-art methods.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"254 ","pages":"Article 110560"},"PeriodicalIF":9.4,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142527451","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robustness analysis of smart manufacturing systems against resource failures: A two-layered network perspective","authors":"Zhiting Song ,&nbsp;Jianhua Zhu ,&nbsp;Kun Chen","doi":"10.1016/j.ress.2024.110595","DOIUrl":"10.1016/j.ress.2024.110595","url":null,"abstract":"<div><div>Complex and changing environments often cause resource failures in smart manufacturing systems (SMSs), significantly affecting their robustness. This paper introduces a novel methodology to assess the robustness of SMSs facing resource failures, using a complex network approach. It divides SMSs into social and technical layers, analyzes resources and relationships within and between these layers, and establishes a two-layered network model. It also categorizes various types of failures and proposes three robustness metrics to evaluate system performance at individual, local, and global levels. Simulations visually demonstrate the methodology and key findings: (1) the robust-but-fragile trait of SMSs only reacts to node failures and keeps significant in terms of the gradient of robustness; (2) there exists no edge failure that keeps damaging system robustness to the maximal or minimal degrees, and edge failures cause less damage to system robustness than node failures; (3) when failures occur, SMS robustness at all levels changes with inconsistent paces, and the optimal link mode varies by network structures and failure strategies. Finally, managerial implications are presented to guide practical robustness control at different stages of SMS lifecycles.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"253 ","pages":"Article 110595"},"PeriodicalIF":9.4,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142530261","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Systematic review and future perspectives on cascading failures in Internet of Things: Modeling and optimization","authors":"Xiuwen Fu ,&nbsp;Dingyi Zheng ,&nbsp;Xiangwei Liu ,&nbsp;Liudong Xing ,&nbsp;Rui Peng","doi":"10.1016/j.ress.2024.110582","DOIUrl":"10.1016/j.ress.2024.110582","url":null,"abstract":"<div><div>Under the influence of cascading failures, the failure of a small number of nodes or components may lead to the paralysis of the entire network system. Cascading failures have become one of the major bottlenecks constraining the long-term reliable operation of Internet of Things (IoT) systems, thus attracting extensive attention from researchers. To better understand the complex mechanisms of IoT cascading failures, diverse models and methods have been proposed. This paper systematically reviews the current research status of cascading failures in IoT, covering various aspects such as network objects, performance metrics, failure states, modeling methods, and network optimization. Additionally, we discuss the limitations in current research on cascading failures in IoT and point out the future research directions.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"254 ","pages":"Article 110582"},"PeriodicalIF":9.4,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142527381","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Team-centered IDAC: Modeling and simulation of operating crew in complex systems – Part 1: Team model and fundamentals","authors":"Mandana Azarkhil ,&nbsp;Ali Mosleh ,&nbsp;Marilia Ramos","doi":"10.1016/j.ress.2024.110541","DOIUrl":"10.1016/j.ress.2024.110541","url":null,"abstract":"<div><div>Operation of highly complex systems such as Nuclear Power Plants (NPPs) generally require highly trained professional operating teams. Factors associated with teamwork, such as ineffective communication and coordination, can be important contributing factors to accidents and unsafe behavior. The impact of crew interactions on team effectiveness and, consequently, on the entire system, has not been fully and quantitatively explored in high-risk environments such as NPPs. Since a team is an interactive social system, team-specific issues must be studied and evaluated from a “team perspective”—based on team dynamics and processes. This paper is part of a two-papers series that presents a simulation-based Team Model for NPP control room operations. The current paper, Part 1, describes the theoretical fundaments of the model and details its elements. The accompanying paper describes the simulation aspects, and a full application of the method to a pipe break accident in a four- four-loop steam generator feedwater system. The proposed model is based on the IDAC (Information, Decision, and Action in Crew context) cognitive model framework. The resulting model, Team-centered IDAC (Tc-IDAC), examines the team activities “Collaborative information collection,” “Shared decision making,” and “Distributed action execution” through specific modules for Team Error Management. These modules include error detection, error indication and error correction, and team performance shaping factors.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"254 ","pages":"Article 110541"},"PeriodicalIF":9.4,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142540143","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}