AI Software That Turns Inspection Data into Actionable Intelligence
FloONE AI Suite analyzes inspection data in real time, diagnoses condition, and enables data-driven decisions for quality, maintenance, and operations.
Capture physical, sensor, and condition signals
Interpret multi-sensor signals with AI
Detect anomalies and assess condition
Manage data and support decisions
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End-to-End EOL Inspection Automation
Automates the full inspection process from serial number identification and measurement to pass/fail assessment and reporting, reducing manual workload and inspection time.
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Defect Pattern-Based Process Traceability
Analyzes accumulated defect data to pinpoint when and where issues occur and trace them back to their root causes in the production process.
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Pre-Shipment Quality Consistency
Detects assembly errors, defective components, and electrical anomalies during EOL inspection to maintain consistent quality before shipment.
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Flexible System Integration
Integrates seamlessly with existing systems and processes, including MES and BIT(Built-In Test).
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Integrated Flight Operations Scheduling
Manages flight planning, reservations, and schedule changes in one place to improve operational efficiency.
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Digital Safety Inspection Management
Digitizes mandatory pre-flight checklists and systematically manages inspection history and completion records.
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AI-Assisted Pre-Flight Decision Support
Assesses motors, ESCs, and blades through non-contact pre-flight diagnostics to support takeoff decisions.
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Parts Management & Predictive Maintenance
Tracks remaining usage, inspection intervals, and condition trends to enable predictive maintenance and improve asset availability.
Modules Composing the FloONE AI Engine
From signal processing and multimodal fusion to anomaly detection, condition assessment, and predictive analytics, AI capabilities come together in a single diagnostic engine.
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1. MODELMultimodal Fusion Sensors
Fuses vibration, current, acoustic, thermal, and other sensor signals to enable more accurate condition assessment.
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2. DATAAI Signal Processing & Noise Reduction
Remove noise and isolate meaningful signals required for accurate diagnostics.
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3. LEARNINGData-Driven Anomaly Detection
Learns from accumulated data to detect anomaly patterns beyond predefined rules.
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4. DIAGNOSEComprehensive Condition Assessment
Combines diagnostic results to assess overall condition using consistent criteria.
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5. CLASSIFYDefect Classification & Root Cause Analysis
Classify detected anomalies by type and identifies the components and root causes behind them.
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6. PREDICTPredictive Analytics
Predicts wear, remaining useful life, and potential failure timing to support proactive maintenance decisions.