How AI Grounded in Approved Engineering Knowledge Speeds Up Boiler Tube Failure Analysis
Discover how AI grounded in approved engineering knowledge speeds up boiler tube failure analysis and supports faster, more reliable diagnosis.
Investigating a boiler tube failure meant an engineer reviewing failure images, gathering operating data, digging through past cases, and reasoning out the root cause from experience; it was slow and dependent on who was doing it. ConforgeLabs designed an AI-assisted RCA workflow that combines computer vision, retrieval, and LLM reasoning, grounded in the organization's own approved RCA cases, with the engineer validating every conclusion.
Every Tube Failure Meant Starting the Investigation Almost From Scratch
When a boiler tube fails, the question is never just that it failed, but why the failure mechanism and root cause have to be identified before the fix is more than a guess. That investigation fell entirely on engineers, and it was a manual, multi-step effort every time: reviewing tube-failure images, collecting the failure and operating information, referring back to previous cases and technical documents, and then reasoning out the RCA from their own expertise. Two problems ran through that. It was slow: assembling images, data, and precedent by hand for each case took real time, time during which the underlying issue could persist. And it was expert-dependent; the quality of an RCA tracked the experience of whoever ran it, so the organization's ability to diagnose failures was effectively capped by the availability of its most seasoned engineers. Underneath sat a deeper inefficiency: the organization had already solved many of these failures before. Approved RCA cases existed, but they lived as static records rather than as knowledge that actively informed the next investigation. Hard-won diagnostic experience wasn't compounding; each new failure was investigated largely as if it were the first of its kind.
“Every failure investigation leaned on whoever had seen something like it before. If that person was busy or had left, we were effectively relearning what we already knew.”
