Detection Performance Baselines Regulators Actually Require

The foundation for any certification argument around an AI-based crack detection system is a rigorously quantified detection performance baseline: regulators require applicants to demonstrate that detection probabilities meet or exceed those achievable by conventional nondestructive inspection (NDI). Without this baseline established before formal submission, the most common outcome is extended regulatory back-and-forth that adds months to the review cycle.

Acoustic emission (AE) is one of the most mature structural health monitoring (SHM) methods, and Cranfield University’s 2014 research on designing AE-based SHM installations established that regulatory acceptance depends on quantitative characterisation of detection performance via the Probability of Hit (POH) metric combined with probabilistic source location methods. The study 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 according to EASA guidance.

Cardiff University’s 2021 work on a real Airbus A320 wing section provides the most operationally specific evidence of what that verification must cover. 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. Aligning this evidence package with FAA AC 25.571 and EASA CS-25 damage tolerance requirements from the outset directly reduces rework during the formal review cycle.

From a damage tolerance perspective, the classical framework that underpins both EASA and FAA structural requirements is clearly defined. As outlined in Purdue University’s 2011 analysis of damage tolerant design and NDI, crack detection systems — including AI-based ones — must define the maximum size of life-limiting defects detectable at a given time, and map this capability 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 the POD baseline prior to formal submission eliminates one of the most common causes of certification delay — the regulator’s inability to map AI outputs to existing structural inspection interval methodology.”

The practical application of these principles is detailed in research from Nile Valley University (2020), which outlines that the DTE 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 DT inputs will substantially reduce the regulatory back-and-forth that otherwise extends review timelines. According to FAA AC 25.571, damage tolerance requirements apply to all principal structural elements, making this alignment non-negotiable for commercial aviation certification.

AI/ML Learning Assurance Under EASA and FAA Frameworks

The central regulatory bottleneck for AI-based crack detection systems is demonstrating that the machine learning model meets an appropriate level of assurance comparable to DO-178C/DO-254 objectives. Both EASA and FAA converge on the concept of “learning assurance,” which must be rigorously documented to shorten review cycles — EASA through its AI Roadmap and published concept papers, and the FAA through FAA ORDER 8110.105A and support for the RTCA SC-216 process.

The most directly applicable evidence comes from Universidad Politécnica de Madrid’s 2022 study, which 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. For a structural crack detection AI system, the same methodology applies: applicants should map each learning assurance step to a corresponding EASA acceptable means of compliance (AMC), producing a compliance matrix deliverable that accelerates formal review.

The Technical University of Munich’s 2021 analysis provides a complementary finding: 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, 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 Design Assurance Level (DAL), materially reducing the volume of certification evidence required and compressing the review schedule.

EMBRAER’s patent portfolio provides direct commercial evidence of how automated structural assessment can be architected to satisfy airworthiness requirements. The 2021 EMBRAER patent on remote and automatic assessment of structural damage and repair describes a system that automates structural analysis to reflect actual damaged and repaired structure behaviour, increasing permissible damage limits and extending overflight periods and inspection intervals. By embedding the automated assessment within the existing DT 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.

“Defining 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 DAL, materially reducing the volume of certification evidence required.”

Modular, Simulation-Based, and Data-Driven Certification 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: Isolate What Changes

Modular certification granularity is the most immediately applicable strategy for AI-based systems. Research from THALES Avionics Electrical Systems (2013) demonstrated 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. Standards bodies including RTCA have incorporated partitioning principles into DO-178C precisely because this approach has proven effective at managing change scope in certified avionics software.

Simulation-Based Compliance: Replace Costly Physical Tests

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) proposed 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 the FAA’s reliance on model-based safety analysis support this approach when simulation fidelity and calibration evidence are adequately documented.

Boeing’s 2023 patent on evaluating structural load on board an aircraft during flight events illustrates how ML-based structural assessment can be embedded in an operationally approved context by using existing structural dynamics models as the physics backbone. An approximator receives flight event parameters and calculates structural response loads using a machine learning algorithm combined with a certified structural dynamics model, with comparison against design load data to determine structural severity and automatic triggering of maintenance action. 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 Consolidation: Calibrate Early

Data-driven design and certification consolidation is a forward-looking strategy addressed by ISAE-SUPAERO research (2021). 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. This produces a system that is inherently calibrated to the certification evidence base rather than requiring post-hoc re-training to match regulatory expectations, a rework cycle that can add significant calendar time to any programme.

Subaru Corporation’s active family of three Japanese patents on flight limit setting driven by structural damage detection provides commercial validation that automated damage-detection-to-flight-restriction systems can receive active regulatory approval. A damage detection unit feeds directly into a flight limit calculation unit that adjusts permissible altitude, speed, and load factor based on detected structural damage severity. The active legal status of this 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.

Patent Landscape: Who Is Shaping Certified AI Inspection

Analysis of over 60 patent records and academic papers reveals a clear stratification of organisations contributing to the technology and certification knowledge base for AI-based structural crack detection. The dominant pattern across all active certified filings is the hybrid architecture: ML inference anchored to certified physics-based structural models. Purely data-driven AI systems without such anchoring are absent from the active certified patent portfolio, suggesting this hybrid approach is currently the dominant certification-viable design pattern.

The Boeing Company dominates the patent space with multiple active filings across jurisdictions — including EP, BR, JP, and 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, which appears to be an effective certification risk management approach validated across multiple regulatory jurisdictions. Research from WIPO on global patent filing trends in AI for aviation confirms that cross-jurisdictional filing strategies are increasingly common for safety-critical AI technologies, reflecting the multi-regulator approval landscape applicants must navigate.

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 DT 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. THALES contributes the most directly process-relevant academic work, with the modular certification granularity framework specifically addressing how to partition software components to minimise the scope of re-certification upon modification — a critical strategy for AI model update management.

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 and certification consolidation). The convergence of these independent research streams toward hybrid ML/physics architectures as the practical path to regulatory acceptance represents a strong consensus signal for programme managers planning AI certification strategies.