Pinnacle & Inspectioneering Webinar: Combining Subject Matter Expertise and Data Science to Optimize CML Inspection

While facilities have access to more data now than ever before, many continue to struggle with getting the most value out of that data. A combination of engineering principles and statistical methods can help to bridge the gap between technological advancements in data analysis and traditional expertise-driven approaches. Leveraging this hybrid approach, it is possible to combine engineering principles with statistical methods to optimize Condition Monitoring Location (CML) placement, inspection, and scheduling. Examples will be presented demonstrating the insights that have been gained by the combination of subject matter expert analysis and data science methods that would not have been seen by either method alone. Attendees will walk away with an understanding of how to incorporate engineering expertise with data science to confidently balance risk and cost to make effective decisions on their corrosion monitoring strategy.

Case Study Highlight

CML Optimization Pilot Project Helps Refinery Reduce Risk and Identify Minimum Reduced Inspection Spend of $384K

CML Prioritization as presented in this case study is a new CML Optimization methodology that predicts future thinning based on past performance from a single CML across repairs and replacements, ranking CMLs by the risk posed to safety and production prior to the next scheduled action (repair/replacement/inspection/turnaround) taken on that CML. This project identified CMLs that indicated asset failure within the next one to two years, prior to the next inspection event, and required immediate attention by the facility. The estimated business impact to the facility resulting from the optimization analysis and resulting prioritization scheme is a minimum of $384,000 over six circuits in reduced inspection spend over the next two turnaround cycles. The savings are due to deferment of non-value-adding inspection activities and inspection scope, resulting from the reduction in recommended CML monitoring as well as a decrease in inspection frequency.

Read the full case study: CML Optimization Pilot Project Helps Refinery Reduce Risk and Identify Minimum Reduced Inspection Spend of $384K

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