Qriton platform
Know what is failing, why, and how long you have.
Diagnose reads sensors, maintenance logs, images, safety context, and operator notes together to produce root cause, severity, remaining useful life, and the evidence behind the recommendation.
Ask Qriton Diagnose
What is driving the vibration spike?
Decision
The pattern is composed of bearing wear and shaft misalignment. Severity, intervention window, uncertainty, and supporting sensor segments are attached to the diagnosis.
Evidence attachedReview state visibleReplay ready
A diagnosis your maintenance team can act on
Go beyond anomaly flags to the cause, urgency, and evidence required to plan an intervention.
A decision plus evidence — not another equipment score.
A warning number still leaves the team to diagnose the problem. Diagnose connects the condition to likely causes, severity, time to intervention, uncertainty, and the source evidence.
Multimodal diagnosis
Reason across telemetry and the human or visual context surrounding the asset.
Pattern composition
Show how multiple failure modes contribute instead of collapsing them into one anomaly.
Actionable timing
Give maintenance teams an intervention window they can plan around.
Intrinsic uncertainty
Make ambiguous cases visible before an overconfident recommendation reaches operations.
Built for the complete decision.
Each capability keeps the operational answer connected to its context, controls, and evidence.
Sensor ingestion
Read streamed or uploaded time series from the equipment already in service.
Maintenance context
Combine work history, operator notes, inspections, and equipment documentation.
Visual evidence
Use images, 3D point clouds, and live safety scenes alongside telemetry.
Root-cause composition
Describe the contributing failure patterns and their relative influence.
Remaining useful life
Translate condition into a practical intervention window.
Replayable records
Tie the diagnosis to model version, evidence, uncertainty, and recommended action.
From signal to accountable action.
Ingest the condition
Bring sensor streams together with logs, notes, imagery, and relevant safety context.
Reach the diagnosis
Evaluate equipment condition, likely causes, severity, useful life, and uncertainty in one path.
Plan the intervention
Give operators a plain-language recommendation and the evidence needed to approve it.
One diagnostic path across the plant’s evidence
Operational data, model reasoning, and the maintenance decision remain connected.
Plant evidence
Sensors, logs, operator notes, images, point clouds, and safety context.
Diagnose reasoning
Condition, failure composition, severity, useful life, and uncertainty.
Maintenance decision
Recommended action, intervention window, supporting evidence, and replay record.
Built for the question that follows the alert: why?
Diagnose keeps the actual reasoning with the recommendation. When a maintenance lead or auditor asks why a part was replaced, the team can open the decision record instead of reconstructing the case from logs.
Discuss an industrial pilotTurn equipment signals into a decision your team can schedule.
Show us the asset, available evidence, failure question, and intervention constraints. We’ll map a focused diagnostic pilot.