Examining the Use of the Technology Acceptance Model for Adoption of Advanced Digital Technologies in Nuclear Power Plants

Kovesdi, Casey

doi:10.1007/978-3-030-80624-8_63

Cited by 4 publications

(4 citation statements)

References 10 publications

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“…Regulatory approval is contingent upon a favorable reception from plant operators and those who work closely with existing systems that the new technology seeks to augment or replace. However, this presents a conflict because familiarity with legacy technology or earlier plant processes creates a user bias: the perceived appropriateness of the advanced AI-guided systems is reduced with more experience using the older system [ 47 ]. Thus, earning the trust of the target user is paramount for engagement.…”

Section: Organizational Readinessmentioning

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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Section: Organizational Readinessmentioning

confidence: 99%

Explainable Artificial Intelligence Technology for Predictive Maintenance

Walker,

Agarwal,

Lin

et al. 2023

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“…Key drivers include the perceived usefulness and perceived ease of use for a given technology to support their task needs (Davis, Bagozzi, and Warshaw 1989). Kovesdi (2021) conceptualized additional drivers that may influence technology acceptance within the nuclear industry. Considerations, including familiarity with a new technology and perceived level of trust and task compatibility (i.e., the degree to which the technology will support a user's task demands), are two salient factors in this adapted TAM framework that drive perceived usefulness and ease of use.…”

Section: Developing the Visionmentioning

confidence: 99%

Demonstration of the Human and Technology Integration Guidance for the Design of Plant-Specific Advanced Automation and Data Visualization Techniques

Kovesdi

Mohon

Elizabeth

2023

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Nuclear power continues to be a safe, reliable, and carbon-free electricity generating source for the United States, though the cost of operating and maintaining the current United States nuclear power plant fleet has become uncompetitive with other sources. This gap is attributed to the advent of new digital instrumentation and control technologies that other electricity generating industries are currently leveraging to streamline work and greatly reduce operating, maintenance, and support costs. Digital instrumentation and control systems and control room modernization offers significant opportunities to reduce operating and maintenance costs to ensure the continued operation of the existing United States light-water reactors.These capabilities enable going beyond like-for-like replacement by offering new ways to transform current work processes through features like increased levels of automation, data analytics and visualization, and decision support. To ensure that the capabilities of the technology and people are being leveraged for safety and reliability, human and technology integration is an important consideration for any major digital main control room modification. This report presents interim findings of two key collaborations with United States utilities currently planning and executing large-scale digital instrumentation and control modifications to their main control rooms. While these collaborations are ongoing, this report presents lessons learned in the demonstration of the human and technology integration methodology.As this work continues, additional lessons learned will be developed. Collectively, this guidance provides industry with human and technology integration and human factors engineering guidance that reduces the technical, financial, and regulatory risk of upgrading the aging instrumentation and control systems to support extended plant life up to and beyond 60 years.

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“…The first is perceived task-technology compatibility, or the successful integration of suitable degrees of automation to complete a task. Extensive experience with legacy technology can negatively impact the perceived compatibility of new technology, which in turn can increase mistrust [29]. The second is a lack of trust in modernization and automation upgrades being completed on time, within budget, and resulting in proven performance improvements.…”

Section: Trust In Automation In Nuclear Operationsmentioning

confidence: 99%

Technical Basis for Advanced Artificial Intelligence and Machine Learning Adoption in Nuclear Power Plants

Agarwal

Walker

Manjunatha

et al. 2022

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The research and development reported here is part of the Technology Enabled Risk-Informed Maintenance Strategy project sponsored by the U.S. Department of Energy's Light Water Reactor Sustainability program. The primary objective of the research presented in this report is to produce a technical basis for developing explainable and trustable artificial intelligence (AI) and machine learning (ML) technologies. The technical basis will lay the foundation for addressing the technical and regulatory adoption challenges of AI/ML technologies across plant assets and the nuclear industry at scale and to achieve seamless cost-effective automation without compromising plant safety and reliability.The technical basis ensuring wider adoption of AI/ML technologies presented in this report was developed by Idaho National Laboratory (INL), in collaboration with Public Service Enterprise Group (PSEG) Nuclear, LLC. To develop the initial technical basis, the circulating water system (CWS) at the PSEG-owned plant sites was selected as the identified plant asset. Specifically, the issue of waterbox fouling diagnosis in the CWS using different types of CWS data is presented to address the said challenge. The approach presented in this report is based on the closed-loop forward-backward process that tries to capture the advancements in data science addressing the explainability of AI/ML outcomes, user-centric interpretability of those outcomes, and how user interpretation can be used as feedback to further simplify the process. A prototype interface is developed to present a focused component-level display of the ML model outputs in a usable and digestible form.

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Examining the Use of the Technology Acceptance Model for Adoption of Advanced Digital Technologies in Nuclear Power Plants

Cited by 4 publications

References 10 publications

Explainable Artificial Intelligence Technology for Predictive Maintenance

Explainable Artificial Intelligence Technology for Predictive Maintenance

Demonstration of the Human and Technology Integration Guidance for the Design of Plant-Specific Advanced Automation and Data Visualization Techniques

Technical Basis for Advanced Artificial Intelligence and Machine Learning Adoption in Nuclear Power Plants

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