Maintenance Log Intelligence
NLP over years of free-text maintenance records — turning the richest failure data in the building into a labeled dataset and a searchable knowledge base.
Results
The Problem
Every plant has years of maintenance history sitting in free-text technician notes — what failed, what was replaced, what the tech thought caused it. It's the richest failure dataset in the building and it's effectively unsearchable. Recurring root causes go unnoticed because nobody can read ten thousand work orders, and the same fix gets rediscovered on every shift.
What We Build
Natural-language processing over historical and incoming maintenance records: normalizing inconsistent terminology, classifying failures into a consistent taxonomy, linking work orders to specific assets and components, and surfacing recurring patterns across lines and sites.
The structured output feeds forward — into predictive maintenance models that previously had no labeled failure history to learn from, and into a searchable knowledge base that puts prior fixes in front of technicians at the point of work.
Outcome
- ◆Years of unstructured maintenance history converted into a labeled failure dataset
- ◆Recurring root causes identified across assets that were previously analyzed in isolation
Techniques
- ◆Named-entity extraction
- ◆Failure taxonomy classification
- ◆Semantic search and retrieval
- ◆Asset-linkage modeling