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.

Product surfaces

Surface	Use in the workflow
Predictive Maintenance module	Reviews equipment profiles, health score, anomaly history, vibration source, energy baseline, maintenance record, template, advisory inbox, and model-console context.
DFS Lite and DFS Pro	Connect historian, sensor, alarm, inspection, and maintenance sources; normalize units; profile signal quality; publish reusable datasets.
FactVerse AI Agent	Uses governed tools to summarize fleet health, retrieve anomaly context, explain risk, and prepare reviewed follow-up actions.
Inspector or maintenance system	Receives approved inspection or maintenance actions and returns completion notes, root cause, replaced parts, and false-positive labels.
Model console	Shows which model or rule set is responsible for an asset, whether it is in production, fallback, unassigned, or blocked, and what data gates prevent retraining.

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.

Continuous operating loop

Predictive maintenance works best as a continuous evidence loop. The system keeps signal ingestion visible, watches model and rule-set readiness, and connects advisory outcomes back to the model and knowledge layer:

• 24x7 signal ingestion keeps current operating history available for health scoring, anomaly review, forecast windows, and diagnosis.
• Advisory review separates raw alerts from actionable maintenance decisions, so teams can accept, reject, or snooze recommendations without losing traceability.
• Work-order outcomes capture true positives, false positives, confirmed root causes, replaced parts, and human override notes.
• Model-console and data-readiness views show whether a model is in production, fallback, unassigned, or blocked by missing data, low coverage, stale signals, or failed quality gates.
• Completed outcomes support model retraining review, threshold tuning, rule-set updates, and knowledge-base improvement after engineering approval.

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.
• Model readiness notes that show active assignment, fallback state, data gate blockers, and recommended data correction.
• Reliability review records such as precision trend, outcome distribution, mean time between failures, mean time to repair, and availability when the customer has enough reviewed history.

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.
• Keep model retraining, threshold changes, and automatic action expansion behind engineering review and change control.

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.