What AI-assisted borescope inspection changes—and what it doesn’t

Executive Summary

Artificial intelligence is shifting remote visual inspection (RVI) from a purely manual, skill-dependent process to a computer-assisted workflow. By automating video and image screening and defect flagging, AI reduces inspector fatigue and standardizes data collection. However, AI acts strictly as a decision-support tool—it does not replace the liability, domain expertise, or nuanced engineering judgment of a certified technician.

What AI Changes (The Operational Shift)

• Defect Detection Speed & Screening: Machine vision models instantly flag anomalies—such as thermal barrier coating (TBC) loss, pitting, cracks, or foreign object debris (FOD)—during live feeds or post-inspection reviews, drastically cutting down inspection times.

• Consistency & Human Fatigue Reduction: Human visual attention drops significantly during repetitive, multi-hour engine or turbine frame inspections. AI provides a consistent secondary review layer, minimizing oversight risks caused by fatigue.

• Standardized Measurement & Reporting: Automated bounding boxes, depth estimation, and surface-defect sizing streamline compliance reporting and ensure data is logged consistently across different crews and facilities.

• Predictive Asset Health Tracking: Historical borescope images tagged by AI create structured visual datasets, making it easier to track crack propagation over time and align maintenance intervals with actual component wear.

What AI Doesn’t Change (The Unaltered Fundamentals)

• Final Disposition & Sign-Off: AI provides predictions, not engineering sign-offs. Regulatory frameworks and airworthiness and safety standards mandate that certified human inspectors remain solely accountable for determining whether a part is service-pass, repair-required, or scrap.

• Understanding Physics & Context: AI identifies surface irregularities, but it cannot evaluate underlying operational mechanics—such as thermal stress patterns, structural load context, or environmental factors causing the defect.

• Hardware & Positioning Capabilities: An AI model cannot fix lighting glare, clear lens obstruction, or manually articulate a flexible probe through tight turbine stages. Quality output remains bottlenecked by probe maneuverability and image capture quality.

• False Positive & Negative Management: Edge cases, glare reflections, and novel damage profiles can trick computer vision models. Technical expertise is critical to validate AI flags and prevent unnecessary teardowns or missed critical flaws.

AI modernizes the mechanics of visual inspection by handling high-volume screening and data structuring. The core responsibility of asset integrity, root-cause analysis, and operational readiness remains firmly in the hands of qualified technical professionals.