A Blockchain-based Traceability System for AI-Driven Engine Blade Inspection

Mahmoud Hafez; Eman Ouda; Mohammed A. Mohammed Eltoum; Khaled Salah; Yusra Abdulrahman

cs.CRMar 9, 2026

A Blockchain-based Traceability System for AI-Driven Engine Blade Inspection

Authors: Mahmoud Hafez, Eman Ouda, Mohammed A. Mohammed Eltoum, Khaled Salah, Yusra Abdulrahman

Organizations: Aerospace Engineering Department, Khalifa University of Science & Technology · Computer and Information Engineering Department, Khalifa University of Science & Technology · Advanced Research and Innovation Center (ARIC), Khalifa University of Science & Technology

Abstract

Aircraft engine blade maintenance relies on inspection records shared across manufacturers, airlines, maintenance organizations, and regulators. Yet current systems are fragmented, difficult to audit, and vulnerable to tampering. This paper presents BladeChain, a blockchain-based system providing immutable traceability for blade inspections throughout the component life cycle. BladeChain is the first system to integrate multi-stakeholder endorsement, automated inspection scheduling, AI model provenance, and cryptographic evidence binding, delivering auditable maintenance traceability for aerospace deployments. Built on a four-stakeholder Hyperledger Fabric network (OEM, Airline, MRO, Regulator), BladeChain captures every life-cycle event in a tamper-evident ledger. A chaincode-enforced state machine governs blade status transitions and automatically triggers inspections when configurable flight hour, cycle, or calendar thresholds are exceeded, eliminating manual scheduling errors. Inspection artifacts are stored off-chain in IPFS and linked to on-chain records via SHA-256 hashes, with each inspection record capturing the AI model name and version used for defect detection. This enables regulators to audit both what defects were found and how they were found. The detection module is pluggable, allowing organizations to adopt or upgrade inspection models without modifying the ledger or workflows. We built a prototype and evaluated it on workloads of up to 100 blades, demonstrating 100% life cycle completion with consistent throughput of 26 operations per minute. A centralized SQL baseline quantifies the consensus overhead and highlights the security trade-off. Security validation confirms tamper detection within 17~ms through hash verification.

Figures & tables

System	Year	Multi-Org	Multi-Org	Maintenance	Parts Life	AI	Off-Chain	Off-Chain
		Actors	Endorsement	Record Keeping	Cycle Mgmt	Integration	Record Storage	Integrity Proofs
Aleshi et al. [ 12 ]	2019	✓	✗	✓	✗	✗	✗	✗
Schyga et al. [ 25 ]	2019	✓	✓	✓	✓	✗	✗	✗
Ho et al. [ 4 ]	2021	✓	✓	✓	✓	✗	✗	✗
AirChain [ 11 ]	2022	✓	✗	✓	✗	✗	✗	✗
Hawashin et al. [ 26 ]	2024	✓	✗	✓	✓	✗	✓	✗

TABLE I : Comparison of blockchain systems for aviation maintenance and traceability.

Fig. 1 : BladeChain architecture. Client applications communicate with the API gateway via REST/SSE. The gateway orchestrates interactions with the Hyperledger Fabric network, IPFS storage, and the pluggable AI inspection engine.

Fig. 2 : Sequence diagram for blade inspection operations. The API gateway orchestrates AI inference, IPFS storage, hash computation, and chaincode invocation before returning results to the client.

Fig. 3 : BladeChain network topology. Peers execute the chaincode and sign. Orderers sequence endorsed transactions into blocks. Peers validate and commit to state database.

Fig. 4 : Blade life cycle stages and permitted transitions. Transitions are enforced by chaincode; the transition to Inspection_Due occurs automatically when usage thresholds are exceeded.

Field	Description
serialNumber	Unique blade identifier (PK)
currentState	Current life cycle state
currentOwner	Operating airline (MSP ID)
manufactureDate	ISO 8601 manufacture timestamp
totalFlightHours	Cumulative flight hours
totalFlightCycles	Cumulative flight cycles

TABLE II : Blade record fields stored on-chain.

Field	Description
eventID	Deterministic identifier
serialNumber	Associated blade serial number
inspectionDate	ISO 8601 inspection timestamp
inspectionType	Type
aiModelName	AI model name
aiModelVersion	AI model version

TABLE III : Inspection event fields recorded on-chain.

Mechanism	Protection
Block hashing	Each block contains the hash of the previous block; tampering invalidates subsequent chain
Endorsement signatures	Committed transactions carry signatures from the majority of organizations
IPFS CID	Content-addressed storage; CID derived from content hash
SHA-256 binding	On-chain hash enables independent artifact verification
gRPC over TLS	Encrypted peer-to-peer and client-to-peer communication

TABLE IV : Data integrity mechanisms.

Threat	Mitigation
Invalid transaction	Rejected by majority endorsement policy
Single compromised peer	Cannot forge transactions without majority endorsement
Forged inspector identity	Inspector/org derived from MSP certificate, not client input
Network eavesdropper	TLS encryption on all channels
IPFS substitution	CID and SHA-256 verification detects modification
Raft node failure	Tolerates $<N/2$ crash faults (1 of 3 orderers)

TABLE V : Threat model: adversary capabilities and mitigations.

Component	Technology	Version
Blockchain framework	Hyperledger Fabric	2.5.14
Consensus	Raft (etcdraft)	3 orderers
Smart contract	Go	1.21.13
State database	Apache CouchDB	3.3.2
API gateway	Node.js + Express	20.19.5
Fabric SDK	fabric-network	2.2.20

TABLE VI : Implementation technology stack.

Panel of Figure 11 — Fig. 5 : Blade images at different processing stages: (a) healthy blade generated using BladeSynth; (b) defective blade generated using BladeSynth; (c) AI inference result for the defective image in (b), showing detected defects annotated with bounding boxes.

Fig. 6 : AI inspection history view (cropped from blade dossier), showing IPFS-stored images and SHA-256 hashes. The full dossier view also includes blade metrics such as operating hours, cycles, ownership, and current state.

Panel of Figure 13 — (a) Dashboard overview

Workload	Completed	Operations
10 blades $\times$ 2 cycles	10/10 (100%)	429
50 blades $\times$ 2 cycles	50/50 (100%)	2,157
100 blades $\times$ 2 cycles	100/100 (100%)	4,075

TABLE VII : Functional validation results.

Workload	Duration	Throughput	Ops/Blade
10 blades	979 sec	26.28 ops/min	42.9
50 blades	4,933 sec	26.24 ops/min	43.1
100 blades	9,476 sec	25.80 ops/min	40.8

TABLE VIII : Scalability evaluation results.

Operation	Count	P50 (ms)	P95 (ms)	Max (ms)
Manufacture	100	2,092	2,102	2,123
Flight log	1,800+	2,085	2,105	2,107
Inspection	200+	2,079	2,095	2,098
Repair	100+	2,080	2,087	2,100
Approval	100+	2,074	2,076	2,076

TABLE IX : Transaction latency by operation type.

Metric	PostgreSQL	BladeChain	Ratio
Duration (100 blades)	92 sec	9,476 sec	103 $\times$
Throughput	3,617 ops/min	25.8 ops/min	140 $\times$
Write latency (avg)	0.72 ms	2,087 ms	2,899 $\times$
Read latency (avg)	0.88 ms	20.9 ms	24 $\times$

TABLE X : Performance comparison: BladeChain vs. PostgreSQL baseline.

Fig. 8 : Tamper detection verification. The recomputed hash from the retrieved artifact differs from the on-chain hash, producing a detectable mismatch.

Fig. 9 : Console output excerpt from the tamper-evidence test, confirming the hash mismatch and verification timing.

Data Provenance

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AI systems are increasingly used to assist consequential decisions in regulated domains such as auditing, finance, and healthcare. This creates a recurring obligation: an organization must be able to reconstruct, after the fact, which evidence informed a given conclusion, and to show that the record of that reasoning was not altered. Existing tools address related but distinct problems - model observability, drift monitoring, governance reporting - and are built for the machine-learning engineer operating a system, not the reviewer who must trace one specific conclusion back to its supporting evidence. We present AuditWeave, a lightweight Python library, with no runtime dependencies, that records the steps of AI-assisted and data-transformation workflows into a single append-only, hash-chained ledger. A small, system-agnostic event vocabulary spans both retrieval-augmented generation (RAG) pipelines and tabular/lakehouse transformations, so a conclusion that draws on both can be traced end-to-end through one record. Within a sealed ledger, any modification, reordering, insertion, or deletion of events is detectable through chain verification. We describe the design and evaluate recording overhead, scalability, and tamper-detection correctness on the reference implementation. The integrity guarantees cost tens of microseconds per event, and, as the hash-chain construction implies, verification flagged every injected mutation across four mutation classes over 2,000 randomized trials.

Abstract

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