Why AI Finds Maintenance Tasks Humans Miss — And Why That Changes Aftersales Forever
For decades, power plants have relied on engineers and consultants to manually convert OEM PDF manuals into maintenance checklists. The goal was simple: extract inspection steps, preventive tasks, and safety procedures and upload them into CMMS and EAM systems. Industrility works with customers in the Power Plant industry where the goal is to extract maintenance tasks from 100s of diverse documents stored in SAP EAM systems.
When we applied AI to the same manuals — covering turbines, generators, transformers, switchgear, pumps, and auxiliary systems — something unexpected happened.
Industrility Aftersales AI didn’t just replicate human output.
It discovered more maintenance knowledge than humans ever captured.
And that changes how digital maintenance should be designed.
This makes me think: This is not just document extraction and automation. This is about expanding human intelligence and building a living maintenance knowledge graph that continuously improves as new manuals, field data, and service reports arrive.
Example 1: Electrical Switchgear — Hidden Preventive Tasks
In a medium-voltage switchgear manual, human extraction teams typically captured standard tasks:
- Annual insulation resistance testing
- Visual inspection of busbars
- Torque checks on terminal connections
- Breaker contact wear measurement
Industrility Aftersales AI analyzed the full document — including footnotes, safety notes, environmental conditions, and appendices — and surfaced additional operational tasks:
- “Inspect partial discharge marks near cable terminations when ambient humidity exceeds 70%” (from a safety warning section)
- “Re-tighten ground bus connections after first 500 operating hours” (buried in commissioning guidelines)
- “Clean air ventilation louvers every 3 months in coal dust environments” (embedded in site preparation recommendations)
These were valid OEM instructions — but rarely made it into structured maintenance programs because they were not located in the “Maintenance” chapter.
AI expanded actionable maintenance coverage by over 25% for this equipment class.
Example 2: Gas Turbines — Semantic Matching of Operational Tasks Hidden Elsewhere
Human-built maintenance schedules typically rely on clearly labeled maintenance sections:
- 4,000-hour inspections
- 8,000-hour hot gas path inspections
- Annual borescope checks
However, Industrility Aftersales AI applies semantic understanding — not just keyword extraction.
In one turbine manual, AI identified the instruction:
“Replace inlet air filter cartridges after 3,000 operating hours.”
This instruction was located inside the installation and pre-commissioning chapter — not the operations or maintenance section.
Humans ignored it because it appeared out of context.
Industrility Aftersales AI classified it correctly as:
- A recurring operational maintenance task
- A preventive condition-based service action
- A runtime-triggered replacement activity
As a result, the task was automatically reclassified into the plant’s operational maintenance schedule where it actually belongs.
This semantic repositioning is critical. It prevents important maintenance actions from being lost simply because OEMs organize manuals inconsistently.
Example 3: Cooling Pumps — Capturing Rare But High-Impact Tasks
For circulating water pumps and auxiliary cooling systems, human extraction usually captures:
- Monthly vibration checks
- Quarterly lubrication
- Annual mechanical seal inspection
Industrility Aftersales AI discovered low-frequency but high-impact tasks hidden across multiple documents:
- “Replace elastomer seals after chemical cleaning cycles” (found in water treatment manual)
- “Inspect impeller erosion after monsoon season operation” (in environmental operation appendix)
- “Recalibrate flow sensors after motor replacement” (in electrical integration guide)
These tasks appear rarely, but when missed, they lead to seal failures, cavitation damage, and unplanned outages.
AI is particularly strong at capturing this long-tail operational knowledge.
Example 4: Cross-Manual Knowledge Linking and Deduplication
In real plants, equipment rarely comes with a single manual.
A typical power plant asset may have:
- OEM equipment manual
- Vendor maintenance handbook
- Commissioning documentation
- Site-specific operating procedures and sections
- Warranty maintenance requirements
Humans treat these as separate documents. Our AI treats them as a connected knowledge graph.
For example:
Across three different manuals for a generator cooling system, AI identified similar tasks:
- “Inspect stator cooling ducts every 6 months”
- “Clean air passages during scheduled outages”
- “Check airflow obstruction quarterly”
Although written differently, our AI semantically recognized them as the same core maintenance activity.
Industrility Aftersales AI system:
- Deduplicated overlapping tasks
- Standardized intervals
- Merged safety steps and tool requirements
- Created one unified operational instruction
This avoids duplicate work orders, conflicting schedules, and inconsistent maintenance execution across departments.
Why Accuracy Is the Wrong Metric
Most AI tools advertise “95% extraction accuracy.”
But matching human output is not the real breakthrough.
What actually matters is:
- How many new maintenance actions were discovered
- How many condition-based triggers were surfaced
- How many hidden operational tasks were reclassified correctly
- How much redundant content was eliminated through deduplication
- How much OEM knowledge was connected across documents
In real power plant use cases in which we were able to dive deep, our fine-tuned AI consistently delivers:
- 20–35% increase in total actionable maintenance steps
- 3x growth in condition-driven service rules
- Significant reduction in duplicate tasks across diverse manuals and catalogs (context-aware)
- Richer context per instruction (tools, safety, conditions, parts)
This is not automation.
This is maintenance intelligence amplification.
The Bigger Shift: From PDFs to Living Operational Knowledge
Power plants do not suffer from lack of data.
They suffer from fragmented, static, and disconnected knowledge.
Industrility Aftersales AI enables a new operating model:
- Manuals become machine-readable knowledge assets
- Maintenance programs update automatically with OEM revisions
- Operational data triggers dynamic maintenance actions
- Digital twins stay synchronized with real-world asset behavior
The result is fewer missed inspections, earlier fault detection, safer work execution, and higher plant availability. We call it “Operational Digital Twin”.
Final Thought
The real value of AI is not that it extracts what humans already know. It’s that it connects, contextualizes, and completes what humans never had time to structure. At Industrility, we are leveraging AI to expand human knowledge in the Industrial world, beyond extraction.
And in asset-intensive industries like power generation, that difference directly translates into uptime, safety, and millions of dollars in avoided downtime.
– Jinesh
