Predictive Maintenance

Predictive maintenance workflows combine operating signals, equipment history, inspection evidence, and maintenance actions. FactVerse AI Agent helps teams interpret risk signals, prepare the next checks, and keep the final decision inside the customer's maintenance governance process.

Operating context

Context	FactVerse source	Why it matters
Equipment identity	FactVerse Platform assets, models, location, ownership, and criticality	Keeps analysis attached to the right asset and operating responsibility
Signal history	DFS time series, events, alarms, and data quality checks	Separates useful trend changes from gaps, stale feeds, or integration noise
Maintenance record	Inspector work orders, inspections, operator notes, and completed actions	Links current risk to what has already been checked or repaired
Knowledge reference	Manuals, SOPs, failure modes, and approved troubleshooting material	Grounds suggested checks in controlled knowledge and removes unsupported guesswork

Prerequisites and source data

• Asset identity, location, criticality, operating owner, and maintenance responsibility are available in FactVerse.
• DFS provides signal history, alarm events, inspection results, and data quality status for the target equipment.
• Inspector or the customer's maintenance system contains recent work orders, completed checks, and operator feedback.
• Approved manuals, SOPs, and troubleshooting knowledge are available for the equipment family.

Execution flow

Workflow

1. Detect a signal change, anomaly, repeated alarm, or maintenance question.
2. Attach asset metadata, recent inspections, operating conditions, and previous work orders.
3. Compare the signal with known failure patterns, data quality status, and recent field evidence.
4. Produce a risk explanation with source references, confidence notes, and missing checks.
5. Prepare an Inspector work order draft or inspection checklist for human approval.
6. Feed the completed action and operator feedback back into the predictive maintenance loop.

Typical outputs

• Risk explanations for pumps, fans, compressors, HVAC equipment, utilities, and production support assets.
• Suggested inspection steps that show why each check is needed.
• Work order drafts with asset context, symptom history, and supporting evidence.
• Data quality notes that highlight missing telemetry, stale values, or inconsistent source mappings.
• Maintenance learning records that connect completed work with later model and knowledge updates.
• Reviewer feedback records that explain why a recommendation was accepted, revised, or rejected for future tuning.

Validation and failure handling

• Validate that the risk explanation cites the signal window, source timestamp, asset identity, and recent maintenance history.
• Mark low-confidence output when telemetry is missing, mappings are stale, or field checks contradict the signal trend.
• Route unclear recommendations to the maintenance owner instead of creating an action draft automatically.
• Capture accepted and rejected recommendations so the predictive maintenance workflow can improve over time.

Governance

Predictive maintenance should be treated as decision support. FactVerse AI Agent can organize evidence and propose next checks, while maintenance owners approve work, schedule downtime, and decide whether a recommendation is operationally appropriate.