An adaptive ARX model to estimate the RUL of aluminum plates based on its crack growth

Barraza-Barraza, Diana; Tercero-Gómez, Víctor G.; Beruvides, Mario G.; Limón-Robles, Jorge

doi:10.1016/j.ymssp.2016.05.041

Cited by 38 publications

(21 citation statements)

References 47 publications

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“…These models are easy to implement with low computational cost, but their performance is heavily affected by the trend information of historical observations, which may be unreliable during incipient failure stage and for long-term forecasts (Baur et al, 2020). Recent examples of using regression-based prognostic models include Qian et al (2014) for bearing wear-out, Barraza-Barraza et al (2017) for crack growth in aluminum plates, Nguyen et al (2018) for NPP steam generator degradation, and Mei et al (2020) for shear building structural damage. In Markovian-based models, the degradation process is assumed to transform within a finite state space that satisfies the Markov (or memoryless) property.…”

Section: Statistical-based Prognosticsmentioning

confidence: 99%

Prognostics and Health Management in Nuclear Power Plants: An Updated Method-Centric Review With Special Focus on Data-Driven Methods

et al. 2021

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In a carbon-constrained world, future uses of nuclear power technologies can contribute to climate change mitigation as the installed electricity generating capacity and range of applications could be much greater and more diverse than with the current plants. To preserve the nuclear industry competitiveness in the global energy market, prognostics and health management (PHM) of plant assets is expected to be important for supporting and sustaining improvements in the economics associated with operating nuclear power plants (NPPs) while maintaining their high availability. Of interest are long-term operation of the legacy fleet to 80 years through subsequent license renewals and economic operation of new builds of either light water reactors or advanced reactor designs. Recent advances in data-driven analysis methods—largely represented by those in artificial intelligence and machine learning—have enhanced applications ranging from robust anomaly detection to automated control and autonomous operation of complex systems. The NPP equipment PHM is one area where the application of these algorithmic advances can significantly improve the ability to perform asset management. This paper provides an updated method-centric review of the full PHM suite in NPPs focusing on data-driven methods and advances since the last major survey article was published in 2015. The main approaches and the state of practice are described, including those for the tasks of data acquisition, condition monitoring, diagnostics, prognostics, and planning and decision-making. Research advances in non-nuclear power applications are also included to assess findings that may be applicable to the nuclear industry, along with the opportunities and challenges when adapting these developments to NPPs. Finally, this paper identifies key research needs in regard to data availability and quality, verification and validation, and uncertainty quantification.

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Section: Statistical-based Prognosticsmentioning

confidence: 99%

Prognostics and Health Management in Nuclear Power Plants: An Updated Method-Centric Review With Special Focus on Data-Driven Methods

et al. 2021

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“…Monte Carlo simulation is particularly relevant for modern applications since it remains valid in nonlinear systems [164]. It can be effectively combined with Hidden Markov models [159] or Bayesian networks [165][166][167]. Kalman filter has been used to increase prediction accuracy, even if it requires a large amount of data to be implemented [168].…”

Section: Prognosis Management and Planmentioning

confidence: 99%

Condition-Based Maintenance—An Extensive Literature Review

et al. 2020

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This paper presents an extensive literature review on the field of condition-based maintenance (CBM). The paper encompasses over 4000 contributions, analysed through bibliometric indicators and meta-analysis techniques. The review adopts Factor Analysis as a dimensionality reduction, concerning the metric of the co-citations of the papers. Four main research areas have been identified, able to delineate the research field synthetically, from theoretical foundations of CBM; (i) towards more specific implementation strategies (ii) and then specifically focusing on operational aspects related to (iii) inspection and replacement and (iv) prognosis. The data-driven bibliometric results have been combined with an interpretative research to extract both core and detailed concepts related to CBM. This combined analysis allows a critical reflection on the field and the extraction of potential future research directions.

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“…Most data-driven predictive maintenance approaches in the literature are based on statistical, probabilistic or machine learning methods and can be categorized into two groups [8]: the first group are prognostic models that directly observe production state processes. They either apply regression-based models [9], or Markovian-based models [10]. Furthermore, Wang et.…”

Section: B Data-driven Predictive Maintenance Approachesmentioning

confidence: 99%

Predicting Time-to-Failure of Plasma Etching Equipment using Machine Learning

Jalali

Heistracher

Strobel

et al. 2019

2019 IEEE International Conference on Prognostics and Health Management (ICPHM)

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Predicting unscheduled breakdowns of plasma etching equipment can reduce maintenance costs and production losses in the semiconductor industry. However, plasma etching is a complex procedure and it is hard to capture all relevant equipment properties and behaviors in a single physical model. Machine learning offers an alternative for predicting upcoming machine failures based on relevant data points. In this paper, we describe three different machine learning tasks that can be used for that purpose: (i) predicting Time-To-Failure (TTF), (ii) predicting health state, and (iii) predicting TTF intervals of an equipment. Our results show that trained machine learning models can outperform benchmarks resembling human judgments in all three tasks. This suggest that machine learning offers a viable alternative to currently deployed plasma etching equipment maintenance strategies and decision making processes.

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An adaptive ARX model to estimate the RUL of aluminum plates based on its crack growth

Cited by 38 publications

References 47 publications

Prognostics and Health Management in Nuclear Power Plants: An Updated Method-Centric Review With Special Focus on Data-Driven Methods

Prognostics and Health Management in Nuclear Power Plants: An Updated Method-Centric Review With Special Focus on Data-Driven Methods

Condition-Based Maintenance—An Extensive Literature Review

Predicting Time-to-Failure of Plasma Etching Equipment using Machine Learning

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