Development of Prognostic Models Using Plant Asset Data

Ramuhalli, Pradeep; Walker, Cody; Agarwal, Vivek; Lybeck, Nancy J.

doi:10.2172/1661211

Cited by 11 publications

(21 citation statements)

References 23 publications

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“…In datadriven methods, no mechanism or input-output relationship needs to be known a priori to produce acceptable results, and the method development/implementation cost is relatively low (Diez-Olivan et al, 2019). Therefore, these methods are highly flexible and can be deployed at any level (component, subsystem, or system level) of the physical asset, which is of particular interest to large, complex systems (Ramuhalli et al, 2020;Sun et al, 2010). As shown in Figure 4, statistical-based and ML/DLbased data-driven methods have attracted most of the research attention in machinery prognostics.…”

Section: Data-driven Methodsmentioning

confidence: 99%

“…Prognostic calculations cannot be done in isolation and depend largely on the stages of monitoring, detection, and diagnostics: the accuracy of these stages will all affect RUL estimation. It is desirable to develop generalizable prognostic methods that can accurately predict the future equipment state given a set of measurements correlated to the equipment's current state (Ramuhalli et al, 2020). An appropriate estimate of the equipment's RUL can improve overall plant performance and reduce costs by optimizing O&M activities.…”

Section: Prognosticsmentioning

confidence: 99%

“…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%

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

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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: Data-driven Methodsmentioning

confidence: 99%

Section: Prognosticsmentioning

confidence: 99%

Section: Prognosticsmentioning

confidence: 99%

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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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“…This report is the last in a series of documents detailing online monitoring, wireless communication networks, and the development of diagnostic and prognostic models using heterogenous data regarding critical balance of plant equipment in NPPs [5][6][7][8][9]. The previous documents in this series evaluated potential wireless technologies and communication networks inside NPPs, based on their performance and economics [5][6][7]; assessed vibration sensors with wireless capabilities [8]; and developed formal methodologies for cleaning data and objectively comparing ML prognostic models [9].…”

Section: Scope Of This Reportmentioning

confidence: 99%

Nuclear Power Fault Diagnostics and Preventative Maintenance Optimization

Walker

Lybeck

Agarwal

et al. 2021

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Operation and Maintenance (O&M) costs for nuclear power plants (NPPs) are very large. The nuclear industry is beginning to see reactors shut down-even after their operating licenses have been extended-because they are not economically competitive with other energy sources. These early closures happen due to economic reasons, despite excellent safety records. Therefore, it is imperative to reduce costs in order to prevent these early closures. This paper showcases recent research on advanced fault diagnostics techniques and preventative maintenance optimization (PMO) for reducing NPP maintenance costs. This report focuses on the feedwater and condensate system (FWCS) for both pressurized-and boiling-water reactor (BWR) systems. The computerized maintenance management system (CMMS), which contains the plant's digital record of all corrective maintenance (CM) and preventative maintenance (PM) work orders, provided the ground truth for locating potential faults and labeling the process data as either healthy or faulted. Various feature extraction techniques were utilized to further differentiate the faulted data from the healthy data. Through a cross-validation procedure, support vectors machines (SVMs) were used to label other test sets of process data as either healthy or faulted. Similar faults were not found within the BWR system, thus opening up the potential for PMO, since an unnecessary amount of PM leads to inflated maintenance costs. This paper summarizes the steps for PMO, from component health determinations to recommendations for action. An example of PMO assessment is presented for condensate pumps (CPs), condensate booster pumps (CBPs), and the respective motors that drive them.

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“…A milestone report was issued in September 2020 [14] documented achievement of the objective to create a prognostic model to estimate predictions of future measurements of the FWCS. This milestone used three different ML methods, long short-term memory (LSTM) networks, SVMs, and a nonlinear autoregressive neural network (NAR) to estimate three different forecast horizons: 1 hour, 1 day, and 1 week.…”

Section: Development Of Prognostic Models Using Plant Asset Datamentioning

confidence: 99%

Analytics-at-scale of Sensor Data for Digital Monitoring in Nuclear Plants: 2nd Annual Report

Walker

Agarwal

Lybeck

et al. 2021

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For economic reasons, the nuclear industry is witnessing premature closure of nuclear power plants, despite excellent safety records. Operations and maintenance activities are some of the largest costs in operating legacy lightwater plants. By reducing operations and maintenance costs, nuclear energy can become more economically competitive with other energy sources. This can be achieved in part by leveraging machine-learning and artificial intelligence technologies to develop data-driven algorithms to better diagnose potential faults within the system. The improved accuracy of the models can lead to a reduction in unnecessary maintenance, thus reducing costs associated with parts, labor, and unnecessary planned, forced, or extended outages.To address these challenges, the goal of this project is to perform research and development in the area of digital monitoring (i.e., the application of advanced sensor technologies, particularly wireless sensor technologies, and data-science-based analytic capabilities) to advance online monitoring and predictive maintenance in nuclear plants and improve plant performance (efficiency gain and economic competitiveness). This report summarizes the Fiscal Year 2020 research progress encompassing the (1) different wireless vibration sensor and data indicators used to assess the health of a plant asset; (2) development of diagnostic models for fault detection; and (3) development of prognostic models for estimating the health of the system up to 7 days ahead.

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Development of Prognostic Models Using Plant Asset Data

Cited by 11 publications

References 23 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

Nuclear Power Fault Diagnostics and Preventative Maintenance Optimization

Analytics-at-scale of Sensor Data for Digital Monitoring in Nuclear Plants: 2nd Annual Report

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