Procedural Guide for System-Level Impact Evaluation of Industrial Artificial Intelligence-Driven Technologies:...

Area of Science:

• Industrial Artificial Intelligence (IAI)
• Condition Monitoring Systems (CMSs)
• Risk Management in Industrial Operations

Background:

• Growing adoption of IAI and complex analysis tools in industrial settings.
• Decision-makers face challenges in objectively evaluating these tools' system-level impacts.
• Condition Monitoring Systems (CMSs) are increasingly vital for asset health but difficult to evaluate.

Purpose of the Study:

• To address the need for objective evaluation of Condition Monitoring Systems (CMSs).
• To present a broader philosophy for evaluating impacts of Industrial Artificial Intelligence (IAI) tools using CMSs as a case study.
• To develop a procedure for assessing CMS suitability based on risk and investment analysis.

Main Methods:

• A risk-based evaluation procedure is presented, using CMSs as a case study.
• The procedure quantifies CMS value by analyzing changes in the likelihood of beneficial and detrimental events.
• Fictional case studies are utilized to illustrate and validate the proposed methodology.

Main Results:

• The proposed procedure enables objective assessment of CMS value without requiring point-wise estimates of future events.
• It provides a framework for evaluating the suitability of CMS for industrial systems.
• Demonstrates the usefulness and validity of the risk-based approach through case studies.

Conclusions:

• Regular, objective review of CMS impacts is essential for industrial facilities to justify investments and maintain competitive advantage.
• The presented risk-based procedure offers a robust method for evaluating IAI tools like CMSs.
• This approach facilitates informed decision-making regarding the implementation and continued use of advanced industrial technologies.