Reliability Engineering & System Safety

Journal: Reliability Engineering & System Safety

Abbreviation

Reliab. eng. syst. saf.

Publisher

Elsevier

ISSN

0951-8320
1879-0836

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Publications 1 - 10 of 69

UQ state-dependent framework for seismic fragility assessment of industrial components
Nardin, Chiara; Marelli, Stefano; Bursi, Oreste S.; et al. (2025)
Reliability Engineering & System Safety
Recently, there has been increased interest in assessing the seismic fragility of industrial plants and process equipment. This is reflected in the growing number of studies, community-funded research projects and experimental campaigns on the matter. Nonetheless, the complexity of the problem and its inherent modelling, coupled with a general scarcity of available data on process equipment, has limited the development of risk assessment methods. In fact, these limitations have led to the creation of simplified and quick-to-run models. In this context, we propose an innovative framework for developing state-dependent fragility functions. This new methodology combines limited data with the power of metamodelling and statistical techniques, namely polynomial chaos expansions (PCE) and bootstrapping. Therefore, we validated the framework on a simplified and computationally efficient MDoF system endowed with Bouc–Wen hysteresis. Then, we tested it on a real nonstructural industrial process component. Specifically, we applied the state-dependent fragility framework to a critical vertical tank of a multicomponent full-scale 3D steel braced frame (BF). The seismic performance of the BF endowed with process components was captured by means of shake table campaign within the European SPIF project. Finally, we derived state-dependent fragility functions based on the combination of PCE and bootstrap at a greatly reduced computational cost.
Domain adaptation via alignment of operation profile for Remaining Useful Lifetime prediction
Nejjar, Ismail; Geissmann, Fabian; Zhao, Mengjie; et al. (2024)
Reliability Engineering & System Safety
Effective Prognostics and Health Management (PHM) relies on accurate prediction of the Remaining Useful Life (RUL). Data-driven RUL prediction techniques rely heavily on the representativeness of the available time-to-failure trajectories. Therefore, these methods may not perform well when applied to data from new units of a fleet that follow different operating conditions than those they were trained on. This is also known as domain shifts. Domain adaptation (DA) methods aim to address the domain shift problem by extracting domain invariant features. However, DA methods do not distinguish between the different phases of operation, such as steady states or transient phases. This can result in misalignment due to under- or over-representation of different operation phases. This paper proposes two novel DA approaches for RUL prediction based on an adversarial domain adaptation framework that considers the different phases of the operation profiles separately. The proposed methodologies align the marginal distributions of each phase of the operation profile in the source domain with its counterpart in the target domain. The effectiveness of the proposed methods is evaluated using the New Commercial Modular Aero-Propulsion System (N-CMAPSS) dataset, where sub-fleets of turbofan engines operating in one of the three different flight classes (short, medium, and long) are treated as separate domains. The experimental results show that the proposed methods improve the accuracy of RUL predictions compared to current state-of-the-art DA methods.
Framework for modeling interdependencies between households, businesses, and infrastructure system, and their response to disruptions—application
Dubaniowski, Mateusz Iwo; Heinimann, Hans Rudolf (2021)
Reliability Engineering & System Safety
Understanding the impact of disruptions to infrastructure systems, households, and businesses is critical to assess resilience of urban systems. However, modeling these complex interdependent systems and applying these models to real-world areas and disruption scenarios is challenging. To address these challenges, we present a workflow for application of a framework for modeling interdependencies between households, businesses, and infrastructure systems, and their impact on disruptions. Furthermore, we identify challenges in applying this framework to an area of Singapore and analyze the resulting cost of 8 disruption scenarios using metrics that we described. Our results showed and confirmed that our model and the devised metrics can be used to assess resilience of systems. In particular, they showed that the multi-system failures have the largest impact on urban areas, and that disruptions affect small vulnerable areas disproportionately. Moreover, they revealed that disruptions to utility systems were costlier than to easily reconfigurable businesses or transportation networks. We also identified and outlined key factors influencing application of the framework to an urban area. We identified some areas for future research focused on investigating a larger set of urban areas and systems.
Explainable AI guided unsupervised fault diagnostics for high-voltage circuit breakers
Hsu, Chi-Ching; Frusque, Gaëtan; Forest, Florent; et al. (2025)
Reliability Engineering & System Safety
Commercial high-voltage circuit breaker (CB) condition monitoring systems rely on directly observable physical parameters such as gas filling pressure with pre-defined thresholds. While these parameters are crucial, they only cover a small subset of malfunctioning mechanisms and usually can be monitored only if the CB is disconnected from the grid. To facilitate online condition monitoring while CBs remain connected, non-intrusive measurement techniques such as vibration or acoustic signals are necessary. Currently, CB condition monitoring studies using these signals typically utilize supervised methods for fault diagnostics, where ground-truth fault types are known due to artificially introduced faults in laboratory settings. This supervised approach is however not feasible in real-world applications, where fault labels are unavailable. In this work, we propose a novel unsupervised fault detection and segmentation framework for CBs based on vibration and acoustic signals. This framework can detect deviations from the healthy state. The explainable artificial intelligence (XAI) approach is applied to the detected faults for fault diagnostics. The specific contributions are: (1) we propose an integrated unsupervised fault detection and segmentation framework that is capable of detecting faults and clustering different faults with only healthy data required during training (2) we provide an unsupervised explainability-guided fault diagnostics approach using XAI to offer domain experts potential indications of the aged or faulty components, achieving fault diagnostics without the prerequisite of ground-truth fault labels. These contributions are validated using an experimental dataset from a high-voltage CB under healthy and artificially introduced fault conditions, contributing to more reliable CB system operation.
Geometric deep learning for online prediction of cascading failures in power grids
Varbella, Anna; Gjorgiev, Blazhe; Sansavini, Giovanni (2023)
Reliability Engineering & System Safety
Past events have revealed that widespread blackouts are mostly a result of cascading failures in the power grid. Understanding the underlining mechanisms of cascading failures can help in developing strategies to minimize the risk of such events. Moreover, a real-time detection of precursors to cascading failures will help operators take measures to prevent their propagation. Currently, the well-established probabilistic and physics-based models of cascading failures offer low computational efficiency, hindering them to be used only as offline tools. In this work, we develop a data-driven methodology for online estimation of the risk of cascading failures. We utilize a physics-based cascading failure model to generate a cascading failure dataset considering different operating conditions and failure scenarios, thus obtaining a sample space covering a large set of power grid states that are labeled as safe or unsafe. We use the synthetic data to train deep learning architectures, namely Feed-forward Neural Networks (FNN) and Graph Neural Networks (GNN). With the development of GNNs, improved performance is achieved with graph-structured data, and GNNs can generalize to graphs of diverse sizes. A comparison between FNN and GNN is made and the GNNs inductive capability is demonstrated via test grids. Furthermore, we apply transfer learning to improve the performance of a pre-trained GNN model on power grids not seen in the training process. The GNN model shows accuracy and balanced accuracy above 96% on selected test datasets not used in the training. Conversely, the FNN shows accuracy above 85% and balanced accuracy above 81% on test datasets unseen during training. Overall, the GNN model is successful in determining, if one or several simultaneous outages result in a critical grid state, under specific grid operating conditions.
Availability of the European power system assets: What we learn from data?
Gjorgiev, Blazhe; Stankovski, Andrej; Wengler, Joe; et al. (2025)
Reliability Engineering & System Safety
The electric power system in Europe is undergoing rapid transformation due to the integration of intermittent and distributed energy sources. This change impacts system operations and the utilization of the system assets. Therefore, there is a growing need to assess the security of electricity supply. In Europe, however, conducting thorough analyses is hindered by the absence of failure data for generation and transmission assets. To overcome this limitation, we use generator and transmission unavailability data from the ENTSO-E transparency platform and quantify plant-specific availabilities of the European power system key assets. In particular, we apply Markov processes and compute the steady-state probabilities of generators, internal lines, interconnectors, transformers, and substations from transition rates estimated for outage events. We found that within the European power system, internal power lines fail and need maintenance less frequently than interconnectors with neighboring power systems. Our analyses also demonstrate that power transformers have higher availability compared to internal lines and interconnectors. We observe that alternate-current interconnectors exhibit notably higher availability than direct-current interconnectors. Additionally, our findings indicate variations in generator availability depending on the generator technology (e.g., fossil, nuclear, hydro, PV, wind). Remarkably, generator failure and repair rates differ based on location. This paper enables further power system security assessments by computing availability parameters (i.e., failure rates, steady-state probabilities) for European generators and transmission grid assets.
Dynamic post-earthquake updating of regional damage estimates using Gaussian Processes
Bodenmann, Lukas; Reuland, Yves; Stojadinovic, Bozidar (2023)
Reliability Engineering & System Safety
The widespread earthquake damage to the built environment induces severe short- and long-term societal consequences. Better community resilience may be achieved through well-organized recovery. Decisions to organize the recovery process are taken under intense time pressure using limited, and potentially inaccurate, data on the severity and the spatial distribution of building damage. We propose to use Gaussian Process inference models to fuse the available inspection data with a pre-existing earthquake risk model to dynamically update regional post-earthquake damage estimates and thereby support a well-organized recovery. The proposed method consistently aggregates the gradually incoming building damage inspection data to reduce the uncertainty in ground shaking intensity geographic distribution and to update regional building damage estimates. The performance of the proposed Gaussian Process methodology is demonstrated on one fictitious earthquake scenario and two real earthquake damage datasets. A comparison with purely data-driven methods shows that the proposed method reduces the number of building inspections required to provide reliable and precise damage predictions.
Bayesian belief networks for human reliability analysis: A review of applications and gaps
Mkrtchyan, Lusine; Podofillini, L.; Dang, V. N. (2015)
Reliability Engineering & System Safety
Using sparse polynomial chaos expansions for the global sensitivity analysis of groundwater lifetime expectancy in a multi-layered hydrogeological model
Deman, G.; Konakli, Katerina; Sudret, Bruno; et al. (2016)
Reliability Engineering & System Safety
A metric for assessing and optimizing data-driven prognostic algorithms for predictive maintenance
Kamariotis, Antonios; Tatsis, Konstantinos; Chatzi, Eleni; et al. (2024)
Reliability Engineering & System Safety
Prognostic Health Management aims to predict the Remaining Useful Life (RUL) of degrading components/systems utilizing monitoring data. These RUL predictions form the basis for optimizing maintenance planning in a Predictive Maintenance (PdM) paradigm. We here propose a metric for assessing data-driven prognostic algorithms based on their impact on downstream PdM decisions. The metric is defined in association with a decision setting and a corresponding PdM policy. We consider two typical PdM decision settings, namely component ordering and/or replacement planning, for which we investigate and improve PdM policies that are commonly utilized in the literature. All policies are evaluated via the data-based estimation of the long-run expected maintenance cost per unit time, using monitored run-to-failure experiments. The policy evaluation enables the estimation of the proposed metric. We employ the metric as an objective function for optimizing heuristic PdM policies and algorithms’ hyperparameters. The effect of different PdM policies on the metric is initially investigated through a theoretical numerical example. Subsequently, we employ four data-driven prognostic algorithms on a simulated turbofan engine degradation problem, and investigate the joint effect of prognostic algorithm and PdM policy on the metric, resulting in a decision-oriented performance assessment of these algorithms.

Publications 1 - 10 of 69

Journal: Reliability Engineering & System Safety

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