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.

翻译：飞机发动机叶片维护依赖于制造商、航空公司、维护机构和监管机构之间共享的检测记录。然而，现有系统存在碎片化、难以审计且易受篡改的问题。本文提出BladeChain，一种基于区块链的系统，为叶片在整个部件生命周期内的检测提供不可篡改的溯源能力。BladeChain是首个集成多方认证、自动化检测调度、AI模型溯源与密码学证据绑定的系统，为航空航天应用提供可审计的维护溯源能力。该系统构建于一个四方（原始设备制造商、航空公司、维护维修大修机构、监管机构）参与的Hyperledger Fabric网络之上，将所有生命周期事件记录于防篡改账本中。通过链码强制执行的状态机管理叶片状态转换，并在可配置的飞行时数、循环次数或日历阈值超出时自动触发检测，从而消除人工调度错误。检测工件脱链存储于IPFS中，并通过SHA-256哈希值与链上记录关联；每条检测记录均包含用于缺陷检测的AI模型名称与版本号，使监管机构能够同时审计发现的缺陷及其检测方法。检测模块采用可插拔设计，允许各机构在不修改账本或工作流程的情况下采用或升级检测模型。我们构建了原型系统，并在多达100个叶片的工作负载上进行了评估，实现了100%的生命周期完成率及每分钟26次操作的稳定吞吐量。通过中心化SQL基准量化了共识开销并揭示了安全权衡。安全性验证表明，通过哈希验证可在17~毫秒内检测到篡改行为。