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

2026-03-09

AI Summary

This study addresses the challenges of trustworthiness in aero-engine blade maintenance, where fragmented systems, vulnerability to tampering, and audit difficulties undermine the reliability of multi-party inspection records. To this end, the authors propose BladeChain—a permissioned blockchain-based traceability system built on Hyperledger Fabric that integrates off-chain storage via IPFS with SHA-256 hash chaining to establish an immutable, four-party trusted network spanning the blade’s entire lifecycle. BladeChain innovatively incorporates a multi-party endorsement mechanism, a chaincode-driven state machine for automated inspection scheduling, AI model version provenance, and cryptographic binding of evidentiary data, enabling plug-and-play AI modules without altering on-chain logic. Experimental results demonstrate that under a workload of 100 blades, the system achieves full lifecycle coverage, sustains a stable throughput of 26 operations per minute, and detects tampering within 17 milliseconds.

Technology Category

• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Multiagent Systems: Mechanism Design

Application Category

• Security and Privacy: Data transparency and provenance
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• traceability
• tampering
• auditability
• engine blade inspection
• maintenance records

Innovation

• blockchain traceability
• AI model provenance
• smart maintenance scheduling
• tamper-evident ledger
• multi-stakeholder endorsement