Why it matters
Without AI, unrecognized faults can escalate, driving AOG events, unexpected brake system reworks, failed audits, cancelled operations, and safety-critical incidents. Early detection curbs escalation and supports regulatory compliance.
From design to safety
Design
AI defines fault detection criteria
The system specifies data requirements from wheel speed transducers, ASCU, and BCV, defines fault thresholds, and documents how fault signals map to maintenance actions, including escalation paths and traceable release criteria for certification.
- Wheel speed transducer
- Anti-skid control unit
- Brake control valve
Build
Sensor integration and certification
During build, wiring harness integrity, ECU interfaces, brake pedal readouts, and fault logging are traced, validated, and certified for release to service with full conformance documentation and independent verification.
- Wheel speed transducer
- Anti-skid control unit
- Brake control valve
- Brake pedal
Operation
Real-time fault scoring
In flight and ground ops, AI continuously correlates wheel speed data with ASCU and BCV logs, applying Bayesian confidence to produce risk scores and recommended alert actions.
- Wheel speed transducer
- Anti-skid control unit
- Brake control valve
- Brake pedal
Maintenance
Preemptive maintenance signals
AI flags trending anomalies, schedules phase inspections, triggers WSS/ALM checks, reviews AD compliance tied to braking systems, and generates service bulletin-like recommendations for operator planning.
- Wheel speed sensor
- Brake control valve
- Anti-skid control unit
Safety
Auditable safety decisions
All AI conclusions are captured with sign-off by the responsible engineer and CAMO, ensuring traceable safety and regulatory compliance with complete decision logs and release package.
- Brake pedal
- Anti-skid control unit
- Wheel speed transducer
Part by part
- 1
Wheel speed transducer
- Watch for
- abnormal wheel speed variance, intermittent readings, or sensor bias under load
- AI action
- flag sensor drift, trigger maintenance check and rebaseline in ASIS data
- 2
Anti-skid control unit
- Watch for
- ASCU output discontinuity, erratic brake pressure, or lockup potential
- AI action
- detect ASCU fault or miscalibration, initiate fault isolation and R&I report
- 3
Brake control valve
- Watch for
- pressure valve stuck, slow response, or leak indicators
- AI action
- prompt BCV test, log pressure trends, and schedule functional test
- 4
Brake pedal
- Watch for
- pedal travel anomaly, non-linear response, or excessive dead band
- AI action
- correlate pedal position with brake pressure, alert for maintenance action
- 5
Brake disc (rotor)
- Watch for
- disc wear beyond limits, scoring, or heat distress signs
- AI action
- calculate wear rate, prompt dimensional inspection and rotor replacement if needed
- 6
Wheel speed sensor
- Watch for
- frequency deviation, side-to-side mismatch, or signal loss
- AI action
- trigger fault isolation, verify wiring, and schedule WSS replacement
Worked example
Catching a seal leak before a charter
Illustrative scenario with sample data.
Autonomous fault trend flags a pressure bleed in the BCV channel during ramp checks on a midsize jet fleet.
Signal in
timestamp:2026-02-14T08:30Z, BCV_pressure: 52 psi, ASCU_status: normal, wheel_speed_diff: 0.8%, pedal_position: 1.2, ambient_temp: 18C, runway_heading: 270
System out
alert: BCV pressure anomaly rising; action: perform functional BCV test; work_order: WO-BCV-0023; recommended_follow_up: schedule PH1 maintenance window and update MEL
Human sign-off: Part-145 certifying engineer approves test plan and CAMO signs off release to service, after reviewing test results and ensuring ATS status is compliant with phase inspection schedule.
How to roll it out
Phase 1
Weeks 1 to 4: Baseline
Establish baseline data, integrate with maintenance reporting, and validate AI fault scoring against known AOG scenarios, ensuring data lineage and version control for all signals.
Phase 2
Weeks 5 to 12: Validation
Run shadow mode on live ops, refine thresholds, document decision logs for regulatory review, and align with OEM service bulletins to maintain compatibility.
Phase 3
Weeks 13 to 20: Active use
Enable proactive maintenance actions, integrate with line and base maintenance planning, and tie to AD compliance engine for automatic paperwork generation.
Where people stay in charge
- Only a qualified engineer or CAMO manager may adjust fault thresholds and escalate safety-critical decisions, because misconfiguration could misinterpret sensor data and endanger flight safety.
- All AI-driven maintenance actions require written authorization from the Part-145 certifying engineer and CAMO before release to service, ensuring regulatory traceability and auditable outcomes.
- The accountable manager must review all safety-critical decisions and sign off on the release plan after audit readiness checks, preserving governance and accountability.
Questions owners and crews ask
What data feeds are required for AI anti-skid fault diagnosis?
The system needs wheel speed transducer data, ASCU outputs, BCV pressure and position, brake pedal readings, and timestamps to correlate events. It also consumes baseline profiles from prior operations and sensor health indicators.
Who approves AI-generated maintenance actions?
A licensed Part-145 certifying engineer approves corrective actions, CAMO validates the plan, and the accountable manager signs off before release to service, with an added requirement to archive decision notes.
Can AI distinguish between sensor fault and actual brake system fault?
Yes, through cross-correlation of multiple sensors, redundancy checks, and historical trend analysis to isolate the root cause, plus confirmation via timed functional tests and AD compliance review.
What records are created for safety regulation?
Event logs, fault codes, maintenance actions, release to service documents, and sign-off records are stored for audits and compliance, alongside CBK entries and traceable change orders.