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Key Elements to Contextualize AI-Driven Condition Monitoring Systems towards Their Risk-Based Evaluation

Published

Author(s)

Mehdi Dadfarnia, Michael Sharp

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

Keywords

condition monitoring systems, algorithm evaluation, asset management, risk assessment, production maintenance

Citation

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 June 17, 2024)

Issues

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Created October 11, 2022, Updated October 19, 2023