A review of prognostics and health management of machine tools

Baur, Marco; Albertelli, Paolo; Monno, Michele

doi:10.1007/s00170-020-05202-3

Cited by 93 publications

(49 citation statements)

References 110 publications

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“…As such, prognostic algorithms can be categorized according to different criteria. Based on the recent publications (Atamuradov et al, 2017;Lei et al, 2018;Taheri et al, 2019;Vogl et al, 2019;Baur et al, 2020;Bektas et al, 2020;Ramuhalli et al, 2020) that contain a comprehensive review of prognostics, these algorithms can be loosely divided into four categories according to their basic techniques or methodologies: physics-based methods, knowledge-based methods, data-driven methods, and hybrid methods.…”

Section: 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: 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 the recent technical literature, a large variety of prognostic applications that estimate time to failure have been reported [13,14,24]. For the experiments discussed here, a novel realtime prognostics algorithm, WTTE-RNN, was deployed.…”

Section: Prognostics Modulementioning

confidence: 99%

A comprehensive framework from real‐time prognostics to maintenance decisions

Jain

Dhada

Hernández

et al. 2021

IET Collab Intel Manufact

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Studying the influence of imperfect prognostics information on maintenance decisions is an underexplored area. To bridge this gap, a new comprehensive maintenance support system is proposed. First, a survival theory-based prognostics module employing the Weibull time-to-event recurrent neural network was deployed in which prognostics competence was enhanced by predicting the parameters of failure distribution. In conjunction with this, a new predictive maintenance (PdM) planning model was framed via a trade-off between corrective maintenance and time lost due to PdM. This optimises maintenance time based on operational and maintenance cost parameters from the historical data. The performance of the proposed framework is demonstrated using an experimental case study on maintenance planning for cutting tools within a manufacturing facility. Systematic sensitivity analysis is provided, and the impact of imperfect prognostics information on maintenance decisions is discussed. Results show that uncertainty about prediction declines as time goes on, and as uncertainty declines, the maintenance timing becomes closer to the remaining useful life. This is expected, as the risk of making a wrong decision decreases over time. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…Physics-based models typically produce high-accuracy results and require less data for tuning when the mechanism is well known [15]. However, physics-based models are usually computationally expensive, especially when applied to system-level prognostic problems or when multiple degradation modes need to be represented.…”

Section: Prognostic Models -Categoriesmentioning

confidence: 99%