Journal of Reliability Science and Engineering

Editor-in-Chief：Chang-Pu Sun
ISSN：3050-2454
Sponsored：
Institute of Systems Engineering of China Academy of Engineering Physics
University of Electronic Science and Technology of China
Hunan University
Beijing Institute of Structure and Environment Engineering
Publication frequency：Quarterly
E-mail：office@jrse.net

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Journal of Reliability Science & Engineering

ISSN：3050-2454 出版周期：Quarterly 期刊基本信息

最新刊期：2026年第3期

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Papers
Dynamic reliability framework for resilience assessment for floating offshore wind turbines AI导读

Zifei Xu, Huahao Ou, Musa Bashir, Michael Beer, Jin Wang, Zaili Yang

DOI：10.1088/3050-2454/ae885a

摘要：Floating offshore wind turbines (FOWTs) are becoming an important part of future energy systems and can complement conventional energy grids in supporting overall energy safety and security. Understanding the dynamic reliability evolution of FOWTs under hazardous events and degradation-induced failures remains largely unexplored and represents a major scientific challenge. Existing monitoring systems, such as SCADA, have limited capability to identify the true root causes of component-level failures. Consequently, maintenance actions may target incorrect or incomplete failure sources, while undetected root causes may continue to propagate residual damage and accelerate degradation of other components. This study develops a dynamic reliability assessment framework for resilience-oriented recovery management of FOWTs under hazardous conditions. A Bayesian network is employed to represent the hierarchical FOWT system and model the propagation of component-level failures to subsystem- and system-level functionality degradation. During recovery, the framework captures the effects of residual damage from unidentified or incompletely repaired root failures on subsequent reliability evolution. A condition-aware deep inspection module is introduced to represent a PHM-enabled recovery process. Although this strategy requires additional inspection effort and decision-making resources before maintenance, it can more accurately identify root causes and propagation pathways, supporting more effective recovery decisions. A case study demonstrates that system reliability can decrease rapidly when critical components are affected by hazardous events. It further shows that experience-based recovery can partially restore system functionality but may fail to eliminate residual damage when root causes are not fully identified. In contrast, the condition-aware strategy achieves greater reliability restoration by improving root-cause identification and repair effectiveness. The results reveal the importance of integrating condition-aware inspection into post-disaster recovery planning and highlight the value of dynamic reliability assessment for resilience improvement in FOWTs. Specifically, the study provides a quantitative understanding of how PHM performance influences recovery effectiveness and resilience improvement, thereby supporting the evaluation of PHM value in post-disaster recovery management of FOWTs.
关键词：floating offshore wind turbines;Bayesian network;condition-aware recovery;energy infrastructure resilience;prognostics and health management

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Papers
Cross-cycle health indicator construction and state of health prediction of lithium-ion batteries by cross recurrence quantification analysis AI导读

Le Wang, Shuaijie Chen, Zhiqiang Cai, Weiyan Liu, Zhenggeng Ye

DOI：10.1088/3050-2454/ae8dd1

摘要：The state of health (SOH) is a key indicator of lithium-ion battery degradation, whose accurate assessment is essential for the reliable operation of battery driven systems. Most existing methods rely on features derived from individual or adjacent cycles, which cannot exploit variation information hidden in monitored signals across different cycles. To address this issue, this paper proposes a novel framework that uses cross-cycle features to estimate the SOH in a derivative manner. Firstly, cross-recurrence quantification analysis (CRQA) is applied to compare battery signals across different cycles, constructing CRQA metric matrix families that reflect cross-cycle variations in raw measurements. Then, the variation of SOH (ΔSOH) between any two cycles is introduced as the prediction target, instead of predicting SOH directly. Next, a convolutional neural network with time series cross-validation is developed to predict ΔSOH, preserving the temporal dependency inherent in ΔSOH. Finally, the proposed framework is implemented and tested on the NASA battery dataset, where the prediction performance is evaluated and compared. The experimental results show a mean absolute error of 0.2965% and a root mean square error of 0.4156%, confirming the feasibility and potential of the cross-cycle health indicator-based indirect approach in battery SOH estimation.
关键词：lithium-ion battery;state of health;cross-recurrence quantification analysis;prediction;cross-cycle features

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Papers
Reliability allocation of multicomponent systems based on non-homogeneous pure birth process under dynamic environment AI导读

Zhou Fang, Wenjie Dong, Yingsai Cao

DOI：10.1088/3050-2454/ae8dd2

摘要：Multicomponent systems operating in dynamic environments are affected by cumulative environmental shocks and dependent component lifetimes, which may lead to inaccurate reliability evaluation and inefficient reliability allocation if ignored. To address this issue, this paper develops a non-homogeneous pure birth process (NHPBP)-driven stochastic time-scale model for multicomponent systems. The environmental shock-counting process is modeled by an NHPBP, while the stochastic time scale is constructed as a path-dependent cumulative exposure functional driven by the whole shock history. Based on the Laplace transform of the stochastic time scale, component survival functions, joint survival functions, and reliability expressions for typical system structures are derived. Positive quadrant dependence among component lifetimes is also established. Furthermore, a cost-oriented reliability allocation model is formulated under a system-level reliability constraint and solved using a genetic algorithm. Numerical experiments on an automotive braking system show that the conventional non-homogeneous Poisson process (NHPP)-based model may overestimate system reliability under cumulative shock amplification, whereas the proposed NHPBP-based allocation model provides feasible allocation schemes under dynamic environments.
关键词：non-homogeneous pure birth process (NHPBP);reliability allocation;system reliability;genetic algorithm (GA)

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