Abstract

Failure analysis in industrial operations has traditionally relied on experience, manual interpretation, and retrospective data. TerraTolga’s Root Cause Copilot™ redefines this process through generative and analytical AI—transforming unstructured maintenance, inspection, and event data into predictive intelligence. The system learns from historical root cause reports, operational logs, and sensor data to recommend solutions, accelerate investigations, and prevent recurrence with unprecedented speed and consistency.

Main Article

Every failure tells a story but the challenge lies in hearing it clearly, before it repeats.
Traditional Root Cause Failure Analysis (RCFA) depends heavily on expert availability, subjective judgment, and inconsistent data entry.
Across process industries, utilities, and energy facilities, this has resulted in lost time, missed insights, and high cost per investigation.

Root Cause Copilot™, part of TerraTolga’s Industrial AI suite, introduces a structured, AI-driven framework that bridges engineering judgment with machine intelligence.

The system operates in three layers:

1. Data Structuring Layer – Uses Natural Language Processing (NLP) to parse years of historical RCFA reports, work orders, and failure logs—extracting relationships between symptoms, causes, and corrective actions.

2. Knowledge Graph & Pattern Recognition – Builds a continuously evolving graph that links component types, operating conditions, materials, and environmental data to recurring failure patterns.
   The system identifies both direct and latent causes—allowing teams to detect issues before they escalate.

3. Generative Copilot Interface – A conversational AI assistant that helps reliability engineers draft full RCFA reports, automatically inserting probable cause chains, recommended actions, and verification steps.
   Each recommendation is traceable to underlying evidence and data lineage, ensuring transparency and auditability.

The Root Cause Copilot™ not only cuts investigation time by more than 60%, but also creates an enterprise memory—an institutional intelligence that never forgets. By combining engineering standards (API, ISO 14224, ASME) with AI-driven learning, it transforms the way organizations manage reliability knowledge, converting lessons learned into lessons applied.

Key Takeaway

TerraTolga’s Root Cause Copilot™ integrates human expertise with AI intelligence to make failure analysis faster, smarter, and self-improving.

It bridges the gap between reactive troubleshooting and predictive reliability, creating the foundation for safer, more resilient industrial systems.