How to improve RBI predictions using a consolidated central repository of failure events

How to improve RBI predictions using a consolidated central repository of failure events.

I have a friend who retired from asset reliability and integrity and now day-trades the spot price of gold. The funny thing is that he uses the Coinbase app. If you subscribe, you can use their targeted AI to determine the price direction and when it will happen. He then bets on the price direction at the predetermined time. The trade executes in 30 seconds, with 95% confidence of a positive outcome. His return is 25%. These predictions are made hours in advance because Coinbase’s AI is constantly monitoring a publicly available dataset.

I am not advocating day-trading; I am advocating the use of targeted AI to monitor loss-of-containment events in real time. I believe it will raise our highest confidence threshold for recommended risk-mitigation actions from 80% to 90% or higher. There is one major obstacle: data.

It is not that the industry has a data scarcity problem; it has a data integration problem. Every loss-of-containment (LOC) incident is evidence. Today, those pieces are scattered across dozens of databases and millions of pages of reports. A targeted AI database could ingest, normalize, and continuously learn from them.

Vision: Global Loss of Containment Event Database/Repository

Mission

Create a single, continuously expanding knowledge database of all publicly available industrial loss-of-containment incidents, and use targeted AI to identify failure trends, predict emerging risks, and improve Risk-Based Inspection (RBI), Mechanical Integrity (MI), and Process Safety Management (PSM).

Data Sources

The database would continuously collect information from:

• U.S. Chemical Safety Board investigations
• OSHA accident investigations
• PHMSA incident reports
• EPA Risk Management Program (RMP) reports
• European eMARS database
• Insurance loss databases
• Industry publications
• Technical papers
• API, ASME, AMPP/NACE publications
• Academic research

AI Normalization Layer

Every incident would be converted into a common engineering schema.

For each event, the AI would identify:

• Equipment type
• Equipment subtype
• Material of construction
• Process fluid
• Operating pressure
• Operating temperature
• Damage mechanism (API RP 571)
• Inspection method
• Inspection interval
• Degradation rate
• Failure mechanism
• Leak size
• Consequence severity
• Root cause(s)
• Human and organizational factors
• Corrective actions
• Applicable standards
• Inspection effectiveness

Regardless of whether the source is a CSB report, a PHMSA record, or an academic paper, the information becomes structured and searchable.

Targeted AI Analytics

Once normalized, the AI can identify patterns that humans cannot easily detect.

For example:

• Which damage mechanisms are showing up more often?
• Which materials fail most often in hydrogen service?
• Which inspection techniques miss certain degradation mechanisms?
• Which combinations of temperature, pressure, metallurgy, and chemistry produce the highest risk?
• Which corrective actions are most effective?
• Which failure mechanisms are becoming more common over time?
• Which facilities have operating conditions similar to historical failures?

From Descriptive to Predictive

Instead of simply reporting what happened, the platform could answer questions like:

"Show every known hydrogen leak involving SA-106 Grade B operating between 550°F and 700°F."

Or:

"Which damage mechanisms are increasingly being identified in renewable diesel units commissioned after 2020?"

Or:

"My hydrotreater operates at 595°F and 250 psig in hydrogen service. Which historical failures most closely match my operating conditions, and what inspections proved most effective?"

Why this is different

Traditional AI is trained on large volumes of homogeneous data.

Industrial AI should be trained on high-value engineering knowledge.

Every loss-of-containment incident is essentially a complete engineering experiment.

• Equipment was designed.
• It operated under known conditions.
• Damage accumulated.
• Failure occurred.
• Experts determined why.
• Lessons were documented.

That is precisely the type of information engineers use to make decisions.

The long-term vision

This could become the industry's equivalent of Bloomberg Terminal for asset integrity.

Instead of tracking stock prices, it would continuously track:

• Global loss-of-containment incidents
• Emerging damage mechanisms
• Inspection effectiveness
• Materials performance
• Equipment reliability
• Mechanical integrity trends
• Process safety indicators
• New industry lessons learned

Rather than replacing API 580, API 581, or API RP 571, the platform would keep them "living" documents by continuously enriching their application with the latest global operating experience. Every new publicly available incident would immediately serve as another training example, enabling the AI to refine failure patterns, identify emerging degradation mechanisms, and provide engineers with recommendations based on the collective experience of the entire process industry rather than the limited history of a single facility.

Be the first to comment

There are no comments for this article.