Scalable Technologies Achieving Risk-Informed Condition-Based Predictive Maintenance Enhancing the Economic Performance of Operating Nuclear Power Plants

Agarwal, Vivek; Manjunatha, Koushik Araseethota; Gribok, Andrei V.; Mortenson, Torrey; Bao, Han; Reese, Randall; Ulrich, Thomas A.; Boring, Ronald L.; Palas, Harry

doi:10.2172/1894498

Cited by 6 publications

(11 citation statements)

References 10 publications

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“…As indicated in (Agarwal, 2021a;2021b), the following two ML models were generated to perform health and fault classification:…”

Section: Margin Model From ML Modelsmentioning

confidence: 99%

Rethinking Reliability in Terms of Margins

Mandelli,

Wang,

Manjunatha

et al. 2023

PHM_CONF

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Current reliability approaches were designed to assess and quantify the reliability associated with complex systems such as nuclear power plants (NPPs). These approaches are generally based on classical Boolean logic structures such as event trees (ETs) and fault trees (FTs) [Rausand, 2020]. The outcome obtained by combining FTs and ETs is the set of minimal cuts sets (MCSs), with each MCS representing a unique combination of BEs that leads to an undesired outcome (e.g., core damage). Probabilistic evaluation of a MCS is performed by evaluating the product of the probability values associated with each BE. A relevant factor here is that the probability values associated with BEs used in the plant models are updated at least every 4 years based on past operational experience and through the use of a Bayesian statistical process [Siu, 1998]. Hence, the probability value of a BE associated with a physical asset (e.g., a centrifugal pump or motor-operated valve) in no way reflects that asset’s actual condition and performance. This fact plays a major role in the application of plant reliability models to support risk-informed decisions. With the particular goal of reducing operation and maintenance costs, existing NPPs are moving from corrective and periodic maintenance toward new types of predictive maintenance strategies [Agarwal, 2021]. This transition is designed such that maintenance is conducted only when the asset requires it (i.e., prior to undergoing imminent failure). And though these benefits cannot be achieved through actual reliability modelling methods and currently employed reliability data, they can be achieved by employing asset-monitoring sensors, automated data acquisition systems, data analysis methods, and improved decision-making processes. Combined, these resources can provide precise information on the health of an asset, track its degradation trends, and estimate its expected failure time. Based on such information, maintenance operations can be scheduled and performed for each asset on an as-needed basis. This dynamic context of predictive maintenance operations requires new methods of data analysis, the propagation of asset health information from the asset level to the system level, and the optimization of plant resources. This paper provides an alternative reliability approach designed for a predictive maintenance context in which a direct link is created between monitoring data and decision-making. Rather than thinking of reliability in terms of system/asset probability of failure, we propose a reliability mindset based on the concept of margin [Mandelli, 2023]. An asset’s health is quantified by determining its margin, based on the asset’s current and historical monitoring data. The margin values of the monitored asset are then propagated through system reliability models (e.g., FTs or reliability block diagrams) to identify the assets that are more critical to guarantee system operation. We show how a margin-based approach can be used assess asset health, based solely on current and historic monitoring data (e.g., condition-based, anomaly detection, diagnostic, and prognostic data) [Xingang, 2021]. A margin-based approach directly addresses the limitations of classical reliability modelling approaches and provides a snapshot of system health—given the availability of monitoring data. These two different approaches are designed to address different types of decisions: classical reliability models support static decisions (e.g., a set frequency of periodic maintenance or surveillance operations) based on past operational experience, whereas a margin-based approach directly supports dynamic decisions involving maintenance operations that should only be performed when necessary, based on monitoring data (i.e., a predictive maintenance context).

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“…As indicated in (Agarwal, 2021a;2021b), the following two ML models were generated to perform health and fault classification:…”

Section: Margin Model From ML Modelsmentioning

confidence: 99%

Rethinking Reliability in Terms of Margins

Mandelli,

Wang,

Manjunatha

et al. 2023

PHM_CONF

View full text Add to dashboard Cite

show abstract

“…GE Hitachi and Exelon are implementing the Predix analytics suite to manage and predict asset performance and enable condition-based maintenance. PKMJ Services, in collaboration with INL and Public Services Enterprise & Group Nuclear LLC, has developed and demonstrated a fully integrated risk-informed condition-based maintenance capability on an automated platform at their Salem NPP [25]. Several advanced reactor developers, such as Kairos Power, X-energy, GE, and Westinghouse, are developing DT-enabling technologies aimed at predictive maintenance, downtime reduction, aging, and degradation management in future reactors.…”

Section: Applicationmentioning

confidence: 99%

Regulatory Considerations for Nuclear Energy Applications of Digital Twin Technologies

Yadav,

Wells,

Pope

et al. 2022

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“…A well-constructed, risk-informed PdM approach (see Figure 1 ) [ 2 ] will take advantage of advancements in sensors, data analytics, machine learning (ML), artificial intelligence (AI), physics-informed modeling, and user-centric visualization approaches. PdM strategies utilize plant assets' current and historical data to develop diagnostic and prognostic models.…”

Section: Introduction and Contributionsmentioning

confidence: 99%

Explainable Artificial Intelligence Technology for Predictive Maintenance

Walker,

Agarwal,

Lin

et al. 2023

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The accomplishments achieved under this research stem from developing innovative solutions that signify advancements in (1) application of AI/ML in nuclear power plants for predictive maintenance, (2) usercentric visualization interface, and (3) quantitative and qualitative measures to achieve explainability and trustworthiness of AI/ML technologies.The report concludes that the development, implementation, and sustainment of advanced AI-guided technologies will require many different types of expertise for maintenance and regulation. Deployment of AI demands bringing together a truly multidisciplinary team of experts to clearly understand broader societal implications.

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Scalable Technologies Achieving Risk-Informed Condition-Based Predictive Maintenance Enhancing the Economic Performance of Operating Nuclear Power Plants

Cited by 6 publications

References 10 publications

Rethinking Reliability in Terms of Margins

Rethinking Reliability in Terms of Margins

Regulatory Considerations for Nuclear Energy Applications of Digital Twin Technologies

Explainable Artificial Intelligence Technology for Predictive Maintenance

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