NIST Researcher Presents Simulation-Based Approach to Assess Condition Monitoring-Enabled Maintenance in Manufacturing

Credit: CTL

At the 2023 International Conference on System Reliability and Safety (ICSRS) in Bologna, Italy, NIST researcher Mehdi Dadfarnia presented research findings that aim to help manufacturers understand the risks and benefits of integrating AI-based condition monitoring systems with their maintenance practices. The research findings make use of the NIST-developed, open-source SimPROCESD software, which is a Python-based discrete-event simulator for multistage manufacturing and equipment maintenance. This conference attracts experts that showcase cutting edge research in risk analysis and reliability engineering.

Dadfarnia’s presentation described the ability to:

  • Compare different AI-based condition monitoring algorithms that enable the same maintenance policy on their algorithm-level metrics (such as accuracy or precision) and ability to improve a manufacturer’s key performance indicator (such as production quantity or product quality).
  • Compare different maintenance policies, including inspection-based and condition-based policies, on their ability to improve a manufacturer’s key performance indicator.
  • Compare the effectiveness of maintenance policies across various manufacturing configurations and shopfloor setups.

This talk presented work from a broader effort in NIST’s Industrial Artificial Intelligence Management and Metrology project, which develops domain-specific tools and methods to improve the effective use of AI systems and tools in industrial applications and to understand their financial and engineering risks and benefits.

For additional details, please see the conference proceedings publication on this work, which can be found at

Comments are closed