Dadfarnia, M.
and Sharp, M.
(2022),
Key Elements to Contextualize AI-Driven Condition Monitoring Systems towards Their Risk-Based Evaluation, 2022 5th IEEE International Artificial Intelligence for Industries (AI4I), Laguna Hills, CA, US, [online], https://doi.org/10.1109/AI4I54798.2022.00017, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=933096
(Accessed November 23, 2024)
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Key Elements to Contextualize AI-Driven Condition Monitoring Systems towards Their Risk-Based Evaluation
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Abstract
Industrial users can be justifiably hesitant in adopting Condition Monitoring Systems (CMSs) unless evidence indicates benefits from their use. Measuring a CMS's ability to prevent losses is difficult and lacks standard procedures. The increasing availability of closed-box Artificial Intelligence (AI)-driven CMSs exacerbates the hesitancy as predicting their impacts is more challenging. This paper details three key elements critical to evaluating CMS impact: (1) the Application Area, (2) the Risk Management Processes, and (3) the Monitoring Mechanism. This paper discusses these elements in their capacity to contextualize a CMS's role within an asset's risk management processes, which can lead to justifying CMS use.Proceedings Title
2022 5th IEEE International Artificial Intelligence for Industries (AI4I)
Conference Dates
September 19-21, 2022
Conference Location
Laguna Hills, CA, US
Pub Type
Conferences
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Keywords
condition monitoring systems, algorithm evaluation, asset management, risk assessment, production maintenance
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Created October 11, 2022, Updated October 19, 2023