AOG events were expensive and often extended by inspection delays and late defect discovery. Traditional maintenance based on rigid scheduled inspections and reactive repairs led to operational inefficiencies. Manual processes varied by technician and shift; documentation was time-consuming.

Key challenges included:

• Costly AOG and inspection bottlenecks
• Rigid scheduled inspections and reactive fault repairs
• Inconsistent documentation and audit readiness
• Need to stay fully compliant with FAA and EASA Part M/145

Datawise deploys hybrid fault-detection and maintenance-optimization systems aligned with research on Aviation Technical Support as a Service (ATSaaS): AI-driven predictive analytics combined with rule-based workflows and MRO integration.

The solution enabled:

• Hybrid fault detection: rule-based + ML/DL for consistent defect identification
• Predictive models using historical and real-time sensor data
• Integration with MRO tools and audit-ready documentation
• Faster turnarounds and reduced AOG; EASA/FAA-aligned

Systems produce audit-ready documentation aligned with FAA and EASA, with research-backed gains in fault detection and operational downtime reduction.

1. 1Hybrid engine: rule-based workflows plus AI-driven anomaly detection
2. 2Real-time sensor data and fault classification support faster defect identification
3. 3Predictive models prioritize maintenance and reduce surprise findings
4. 4Integration with MRO tools and regulatory documentation (Part M, Part 145)

• Research-backed hybrid fault detection significantly outperforms customization-only or personalization-only approaches
• Substantial reduction in AOG and operational downtime vs. baseline
• Operational data for continuous improvement and audit trail
• Stronger defect capture in pre-checks vs. manual-only inspection

• Less AOG and faster turnarounds
• Stronger compliance and audit readiness (EASA, FAA)
• Data-driven maintenance and predictability
• Scalable for both large airlines and small–medium operators