Reduce No Fault Found Returns in Industrial Equipment: 30% Fewer Unplanned Outages
Every unnecessary RMA drains warranty reserves and clogs your refurbishment pipeline.
In Brief
AI failure-pattern recognition delivers 30% fewer unplanned outages and 65% faster fault detection in under 10 minutes, per Bruviti deployment data. Operators cut No Fault Found warranty returns by catching the real fault before a part ships back, so warranty queues fill with verified failures instead of phantom ones.
The Cost of Unnecessary Returns
Invalid Return Processing
Equipment returned under warranty arrives at refurbishment centers with no detectable fault. Reverse logistics, inspection, and restocking costs accumulate for returns that never should have been authorized.
Refurbishment Backlog
NFF returns consume inspection capacity that should be devoted to genuine failures. Queue times stretch as your team processes equipment that could have stayed in the field, delaying real repairs.
Warranty Reserve Erosion
Unpredictable NFF volume makes warranty cost forecasting unreliable. Finance accrues reserves based on historical claims, but invalid returns inflate those numbers beyond actual equipment failure rates.
Pre-Authorization Failure Verification
The platform ingests warranty claim descriptions and cross-references them against equipment telemetry logs, maintenance history, and past failure patterns. When a claim arrives describing intermittent motor vibration on a CNC machine, the system checks sensor data from the week before the claim. If accelerometer readings show normal vibration profiles and no deviation from baseline, the claim gets flagged for additional diagnostics before issuing an RMA.
Claims processors see a pre-authorization diagnostic score that separates genuine failures from installation errors, operator misuse, or environmental conditions. The workflow presents verification steps tailored to the claim type—voltage logs for electrical symptoms, temperature profiles for thermal failures, cycle counts for wear patterns. You approve or deny RMAs based on evidence, not just claim descriptions.
What You Gain
See It In Action
Defect Detection from SEM/AFM Images
Automate microscopic failure analysis for turbine blades and compressor components, identifying genuine metallurgical defects versus wear-and-tear to validate warranty claims.
Warranty Claims Coding
Classify claims by failure mode, component type, and root cause to route high-NFF categories for pre-authorization verification and streamline valid claims.
Application for Industrial Equipment OEMs
Where NFF Costs Hit Hardest
Industrial equipment operates in harsh environments where environmental factors—voltage fluctuations, temperature extremes, contamination—often mimic component failures. Customer service teams authorize RMAs based on reported symptoms, but returned CNC controllers, hydraulic pumps, or PLCs frequently test within specification at the refurbishment center.
Long equipment lifecycles amplify the problem. A compressor manufactured 15 years ago may have obsolete sensors or undocumented firmware revisions, making failure diagnosis dependent on tribal knowledge. When that knowledge walks out the door, NFF rates climb as processors err toward authorizing returns rather than risk denying valid claims.
Implementation Priorities
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Start with highest-volume equipment lines to reduce NFF costs fastest
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Connect SCADA and PLC telemetry feeds for real-time failure context
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Measure NFF rate reduction and warranty reserve accuracy over 90 days
