Journal: IEEE Transactions on Reliability

Abbreviation

IEEE trans. reliab.

Publisher

IEEE

Journal Volumes

ISSN

0018-9529
1558-1721

Description

Filters

2011 - 201952020 - 20254
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Publications1 - 9 of 9
  • Fuzzy Classification With Restricted Boltzman Machines and Echo-State Networks for Predicting Potential Railway Door System Failures
    Item type: Journal Article
    Fink, Olga; Zio, Enrico; Weidmann, Ulrich (2015)
    IEEE Transactions on Reliability
  • Learning Informative Health Indicators Through Unsupervised Contrastive Learning
    Item type: Journal Article
    Rombach, Katharina; Michau, Gabriel; Bürzle, Wilfried; et al. (2025)
    IEEE Transactions on Reliability
    Monitoring the health of complex industrial assets is crucial for safe and efficient operations. Health indicators that provide quantitative real-time insights into the health status of industrial assets over time serve as valuable tools for, e.g., fault detection or prognostics. This article proposes a novel, versatile, and unsupervised approach to learn health indicators using contrastive learning, where the operational time serves as a proxy for degradation. To highlight its versatility, the approach is evaluated on two tasks and case studies with different characteristics: wear assessment of milling machines and fault detection of railway wheels. Our results show that the proposed methodology effectively learns a health indicator that follows the wear of milling machines (0.97 correlation on average) and is suitable for fault detection in railway wheels (88.7% balanced accuracy). The conducted experiments demonstrate the versatility of the approach for various systems and health conditions.
  • Fast Bayesian Inference of Reparameterized Gamma Process With Random Effects
    Item type: Journal Article
    Zhou, Shirong; Xu, Ancha; Tang, Yincai; et al. (2024)
    IEEE Transactions on Reliability
    In the field of reliability engineering, the gamma process plays an important role in modeling degradation processes. However, extracting lifetime information from product degradation observations has long been suffering from both ineffective modeling techniques and inefficient statistical inference methods. To overcome these challenges, we propose a reparameterized gamma process with random effects in this article. Compared with the classical gamma process, the proposed model has a more intuitive physical interpretation. In addition, statistical inference for the model can be readily done through the variational Bayesian algorithm. Combining with the Gauss-Hermite quadrature and the Laplace approximation, the algorithm yields closed-form variational posteriors for the proposed model. Its superiority over two other inference methods (expectation maximization and Monte Carlo Markov Chain) in terms of computational efficiency and estimation accuracy is demonstrated by simulation.
  • A Classification Framework for Predicting Components' Remaining Useful Life Based on Discrete-Event Diagnostic Data
    Item type: Journal Article
    Fink, Olga; Zio, Enrico; Weidmann, Ulrich (2015)
    IEEE Transactions on Reliability
  • An FTA Method for the Unavailability of Supply in Gas Networks Supported by Physical Models
    Item type: Journal Article
    Gjorgiev, Blazhe; Antenucci, Andrea; Volkanovski, Andrija; et al. (2020)
    IEEE Transactions on Reliability
    This paper presents a fault tree analysis (FTA) method for the unavailability of supply of gas networks. The method is based on the automatic generation of fault trees, which estimate the probability of disruption of the gas delivery from terminals/storages to the demand nodes. Moreover, it allows probabilistic analyses of the availability of gas supply to individual demand nodes and to the overall gas network. To assess the importance of each network component, the risk achievement worth and the risk reduction worth importance measures are utilized. The developed method can identify weakness in the gas network and can guide expansion planning and maintenance scheduling activities. Furthermore, the developed framework is enriched by steady-state analysis of the gas network operations performed using a physical flow/pressure model. The impact of a component failure on the gas supply interruption at different demand nodes is assessed by the physical model in retrospective to the FTA results. The framework is exemplified with reference to the reduced gas network of Great Britain. The results provide insights to support a robust reliability assessment of the gas network. Moreover, the probability mapping of the most important components in the gas network informs optimal strategies for maintenance scheduling as well for prioritizing improvements of the gas network aimed at significant risk reduction. Remarkably, the failure of components identified as most important according to the FTA importance measures may not have significant effects on the gas supply. A discussion on the range of applicability and limitations of the two methodologies is provided.
  • Modeling Interdependent Network Systems for Identifying Cascade-Safe Operating Margins
    Item type: Journal Article
    Zio, Enrico; Sansavini, Giovanni (2011)
    IEEE Transactions on Reliability
  • Resilience-Based Component Importance Measures for Critical Infrastructure Network Systems
    Item type: Journal Article
    Fang, Yi-Ping; Pedroni, Nicola; Zio, Enrico (2016)
    IEEE Transactions on Reliability
  • Reliability Evaluation and Maintenance Planning for Systems with Load-Sharing Auxiliary Components
    Item type: Journal Article
    Cai, Xiayu; Shen, Jingyuan; Shen, Lijuan (2023)
    IEEE Transactions on Reliability
    In many engineering systems, aside from the main component fulfilling the essential functions, a number of auxiliary components are configured to protect the main component and improve the reliability of the system. In actual operation, the failure or state change of the auxiliary components may affect the reliability both the main component and the remaining operational auxiliary components. However, the structure and dependence between the auxiliary components has been ignored in the existing studies. To fill this gap, we consider a system with a main component and a protective auxiliary subsystem. The latter is a load-sharing k-out-of-n system, that is, there is dependence between the auxiliary components. For such a system, an opportunistic inspection and preventive maintenance strategy is proposed. Then, we derive the system reliability using the Laplace transforms and the matrix method. The long-run average cost of the system is then derived, based on which the optimal maintenance problem is formulated and solved by an enumeration method. A numerical example, together with sensitivity studies of some model parameters, shows how the evolution of the parameters influences the optimal maintenance strategy. Finally, the model is extended by introducing periodic inspection and preventive maintenance strategy for main component, and the two strategies are compared.
  • Dynamic Defense Resource Allocation for Minimizing Unsupplied Demand in Cyber-Physical Systems Against Uncertain Attacks
    Item type: Journal Article
    Mo, Huadong; Sansavini, Giovanni (2017)
    IEEE Transactions on Reliability
Publications1 - 9 of 9