Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

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

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

TL;DR

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.

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.

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

TL;DR

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.

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.
Paper Structure (46 sections, 12 figures, 10 tables, 2 algorithms)

This paper contains 46 sections, 12 figures, 10 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Related Work
  3. Blockchain for Aircraft Parts Traceability
  4. Blockchain for Aircraft Maintenance Records
  5. Blockchain in MRO Organizations
  6. Positioning of BladeChain
  7. System Design
  8. Design Requirements
  9. Design Decisions
  10. System Architecture
  11. Inspection Data Flow
  12. Network Topology
  13. Organizations
  14. Channel Configuration
  15. Ordering Service
  16. ...and 31 more sections

Figures (12)

  • Figure 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.
  • Figure 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.
  • Figure 3: BladeChain network topology. Peers execute the chaincode and sign. Orderers sequence endorsed transactions into blocks. Peers validate and commit to state database.
  • Figure 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.
  • Figure 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.
  • ...and 7 more figures