Establishing Detection Performance Baselines Regulators Will Accept

The foundation for any certification argument around an AI-based crack detection system is a rigorously quantified detection performance baseline — and regulators require applicants to demonstrate that detection probabilities meet or exceed those achievable by conventional nondestructive inspection (NDI). This single requirement, if not addressed from the outset, is one of the most frequent causes of schedule overrun during formal review.

The acoustic emission (AE) technique is one of the most mature structural health monitoring (SHM) methods, and its application to aircraft structures has been studied in depth. Research from Cranfield University (2014) established that the key to regulatory acceptance of AE-based monitoring is quantitative characterisation of detection performance via the Probability of Hit (POH) metric combined with probabilistic source location methods. That study also found that performance in complex structural geometries with variable service environments is not well understood — making documented, repeatable performance verification an essential pre-certification activity that can consume significant calendar time if not planned early.

Cardiff University’s 2021 work on a real Airbus A320 wing section clarifies what specifically must be validated. Installation of AE monitoring demonstrated that the Delta-T source location technique reduced average location error from 85mm to 23mm compared to commercial standard methods. Critically, fatigue testing exposed residual performance gaps not visible in static trials — underscoring that certification evidence must include fatigue loading conditions representative of actual service. Establishing this evidence package early and aligning it with FAA AC 25.571 and EASA CS-25 damage tolerance requirements directly reduces rework during the formal review cycle, according to EASA‘s published acceptable means of compliance.

From a damage tolerance perspective, the classical framework that underpins both EASA and FAA structural requirements is clearly defined: inspection thresholds, intervals, and NDI capability must be linked to fracture mechanics. As outlined by Purdue University (2011), damage tolerance requires that NDI methods — and by extension AI-based detection systems — define the maximum size of life-limiting defects detectable at a given time, and that this capability is mapped to anticipated service loads and desired service life. An AI crack detection system must therefore produce a quantitative probability-of-detection (POD) curve that can be inserted directly into the damage tolerance evaluation (DTE) process to define or validate inspection intervals, exactly as a traditional NDI method would. Establishing this POD baseline prior to formal submission eliminates one of the most common causes of certification delay.

The practical application of these principles to inspection interval derivation is detailed in research from Nile Valley University (2020), which outlines that the damage tolerance evaluation process requires a clear methodology progressing from structural load characterisation, to defect size definition, to inspection capability quantification. Applicants who front-load this analysis and align AI system outputs with these damage tolerance inputs will substantially reduce the regulatory back-and-forth that otherwise extends review timelines.

Navigating EASA and FAA Learning Assurance Requirements

The central regulatory bottleneck for AI-based structural crack detection systems is demonstrating that the machine learning model meets an appropriate level of assurance comparable to DO-178C/DO-254 objectives — and the most direct path through this bottleneck is to structure the entire ML development lifecycle as an auditable compliance trail aligned with EASA’s published learning assurance user guide.

EASA has led the international regulatory community with its AI Roadmap and published concept papers, while the FAA has issued FAAORDER 8110.105A and supports the EACO/RTCA SC-216 process. Both converge on the concept of “learning assurance,” which must be rigorously documented to shorten review cycles. Research from the Universidad Politécnica de Madrid (2022) applied EASA’s learning assurance user guide to a machine learning classification and regression model for air traffic conflict detection based on extreme gradient boosting. The authors demonstrated that structuring the development lifecycle around the EASA learning assurance process — covering data management, model training, validation, and explainability — creates a directly auditable compliance trail.

“A design decision to define the AI output as a probabilistic risk indicator — feeding into the damage tolerance framework rather than replacing it — can re-classify the system at a lower DAL level, materially reducing the volume of certification evidence required and compressing the review schedule.”

The Technical University of Munich provided a complementary analysis in 2021, demonstrating that all objectives of current airborne certification standards can be achieved for low-criticality ML-based systems if specific assumptions about the ML development workflow are applied. The paper identifies fundamental incompatibilities between traditional design assurance approaches and certain ML characteristics — particularly the statistical nature of ML outputs versus the deterministic behaviour assumed by DO-178C — and proposes targeted adaptations. For structural crack detection systems, positioning the AI output as a probabilistic risk indicator that feeds into the damage tolerance framework rather than replacing it can re-classify the system at a lower Design Assurance Level (DAL), materially reducing the volume of certification evidence required. According to FAA and EASA guidance on DAL classification, this architectural choice is one of the highest-leverage decisions available to a certification programme manager.

EMBRAER’s patent portfolio provides direct commercial evidence of how automated structural assessment can be architected to satisfy airworthiness requirements. EMBRAER’s 2021 patent describes a system that automates structural analysis to reflect actual damaged/repaired structure behaviour, increasing permissible damage limits and extending overflight periods and inspection intervals. By embedding the automated assessment within the existing damage tolerance and structural repair manual framework, EMBRAER demonstrates a certification strategy that positions the AI component as an enhancement to — rather than a replacement of — approved analysis methods, thereby avoiding the need for a wholly new certification basis. This architectural approach directly reduces certification timeline risk and has received active regulatory status in Brazil.

Certification Process Engineering: Modular, Simulation-Based, and Data-Driven Strategies

Even with technically sound AI systems and robust learning assurance documentation, process inefficiencies in the certification pathway itself are a major source of delay. Three established strategies from the aviation industry directly address this: modular certification with isolated change scope, simulation-based compliance demonstration, and data-driven consolidation of design and certification activities.

Modular Certification Granularity

Modular certification is the most immediately applicable strategy for AI-based systems. THALES Avionics Electrical Systems demonstrated in 2013 that isolating the component most likely to change — in this case the ML inference engine or model weights — from the stable, certified host platform allows the AI component to be certified independently and updated without triggering a full recertification of the application. For structural crack detection systems, this means architecting the system so that the AI inference module is partitioned from the certified sensor acquisition hardware and data communication buses. Regulatory submissions can then focus on the learning assurance evidence for the AI module alone, with the rest of the system covered by existing approvals, substantially reducing the scope of each regulatory review.

Simulation-Based Compliance Verification

Simulation-based compliance verification offers a second pathway to reducing calendar time by replacing or supplementing costly physical test campaigns. Research from the Air Force Engineering University, Xi’an (2012) proposes a virtual flight testing method connecting MATLAB/Simulink and FlightGear to establish verification scenarios based on airworthiness criteria, including modelling of small-probability critical events. For crack detection systems, simulation of structural damage scenarios — generating synthetic crack images or sensor signatures representative of the full spectrum of in-service damage states — can replace or supplement physical specimen testing programmes, generating POD curves at a fraction of the cost and time. Both EASA’s AMC 20-189 guidance on advanced technology and FAA’s reliance on model-based safety analysis support this approach when simulation fidelity and calibration evidence are adequately documented, as confirmed by published guidance from EASA.

Anchoring ML to Certified Physics-Based Models

Boeing’s 2023 patent on structural load evaluation during flight events illustrates how machine learning-based structural assessment can be embedded in an operationally approved context by using existing structural dynamics models as the physics backbone. In this architecture, an ML approximator receives flight event parameters and calculates structural response loads using a machine learning algorithm combined with a certified structural dynamics model. The comparison against design load data to determine structural severity — and the automatic triggering of maintenance action — keeps the AI system within the regulatory envelope of the existing structural analysis. This hybrid architecture, anchoring ML outputs to certified physics-based models, is directly applicable to crack detection systems and is a recognised strategy for reducing the novel aspects that regulators must evaluate.

Data-Driven Design and Certification Consolidation

ISAE-SUPAERO research (2021) demonstrates that incorporating operational data into preliminary design and certification steps reduces certification delays by ensuring the system is designed against realistic in-service conditions from the outset. For AI crack detection, this translates to using operational fleet data — historical maintenance records, inspection findings, structural test results — as training and validation data from the beginning of the AI development cycle, producing a system that is inherently calibrated to the certification evidence base rather than requiring post-hoc re-training to match regulatory expectations. Organisations such as ICAO have similarly emphasised the importance of operational data integration in continuing airworthiness frameworks.

Who Is Filing — and What Their Architecture Choices Reveal

Analysis of the source dataset reveals a clear stratification of organisations contributing to the technology and certification knowledge base relevant to AI-based structural crack detection, and their patent architecture choices signal which approaches regulators have accepted.

The Boeing Company dominates the patent space with multiple active filings across jurisdictions (EP, BR, JP, KR) covering structural load assessment using machine learning, automated inspection with data validation, fault detection, and cumulative failure effects assessment. Boeing’s strategy consistently embeds ML within approved physics-based frameworks — a pattern that appears to be an effective certification risk management approach and that directly informs the hybrid architecture recommendation above.

EMBRAER S.A. holds active Brazilian patents on remote and automatic structural damage assessment, demonstrating that an OEM-level approach to automating structural repair disposition can achieve active regulatory status by integrating within existing damage tolerance and repair manual frameworks. Subaru Corporation maintains an active family of three Japanese patents on flight limit setting driven by structural damage detection — one of the few examples in the dataset of damage-detection-to-operational-action systems with confirmed active legal status. The active status of this Subaru patent family confirms that regulators have accepted the architectural principle of damage-detection-driven operational restrictions, establishing a precedent that AI-based crack detection outputs can serve as direct inputs to approved maintenance and operational decision frameworks.

A notable convergence trend across both industry patents and academic literature is the emergence of hybrid architectures — ML inference anchored to certified physics-based structural models — as the practical path to regulatory acceptance. Purely data-driven AI systems without such anchoring appear absent from the active certified patent portfolio, suggesting this hybrid approach is currently the dominant certification-viable design pattern. This finding is consistent with published RTCA SC-216 work on ML airworthiness, which similarly emphasises the need for deterministic safety boundaries around probabilistic ML components.

Academic contributors providing the highest-impact regulatory and technical evidence include: Universidad Politécnica de Madrid (EASA learning assurance case study), Technical University of Munich (ML certification standards analysis), Cranfield University (SHM installation methodology), Cardiff University (AE source location on Airbus structures), Purdue University (damage tolerance and NDI framework), and ISAE-SUPAERO (data-driven design/certification consolidation). Together, these institutions provide the published evidence base that certification applicants can cite directly in their compliance documentation.