Inspection, Monitoring & Health Management of Aerospace Systems

Learning Outcomes

Full CEng Programme Level Learning Outcomes

Having successfully completed this module you will be able to:

• M14 (quality management & continuous improvement) → LO6 [assessed via continuous assessment]
• M2 (problem analysis with incomplete data) → LO1, LO2, LO3 [assessed via continuous assessment and final exam]
• M17 (communication to technical & non-technical audiences) → LO6 [assessed via continuous assessment]
• M6 (systems approach/integrated systems perspective) → LO3, LO4 [assessed via continuous assessment and final exam]
• M12 (practical laboratory skills) → LO4, LO5 [assessed via continuous assessment]
• M8 (ethical responsibility) → LO5 [delivered/developed but not assessed]
• M3 (computational/analytical model selection) → LO1, LO2 [assessed via continuous assessment and final exam]
• M1 (advanced science & engineering principles) → LO1, LO2 [assessed via continuous assessment and final exam]
• M9 (risk evaluation & mitigation) → LO4, LO5 [assessed via continuous assessment]

Aerospace Systems & Digitalization a) Overview of Aerospace Systems b) Data Centricity of Aerospace Systems c) Aerospace Operation and Digital Transformation d) Sustainability in Aerospace and Aviation e) Case Studies on Digital Transformation Sensing & Instrumentation a) Sensors in Aerospace Engineering b) Fly-by-wire vs Mechanical c) Sensors and Transducers d) Signal Conditioning and Signal Processing e) Analog and digital filters f) Measurement chain g) Case Studies on sensors and sensing – data collection NDT & E and Monitoring a) Introduction to NDT & E b) Visual Inspections c) Liquid Penetrants d) Ultrasonics e) Radiography f) Infrared Thermography g) Case Studies on NDT data analysis and interpretation h) Structural Health Monitoring i) Data Acquisition & Data Management j) Case Studies on NDT signal processing Automated Inspection & Diagnostics a) Automated – Autonomous Systems (ground and/or aerial based) used in inspections b) Self Localization & Obstacle Avoidance in Complex Environments - Hangar of the Future c) Aerial contact and non-contact based inspection d) The importance of digitalization in inspections and ML Approaches for Damage Assessment e) Defect localisation and image processing f) Smart hangar localisation and sensing for robotics-assisted inspection g) Case Studies on automated inspection and decision making Digital Twins & Reasoning a) Overview of Digital Twins b) Digital Design Patterns and Types of Digital Twins c) Digital Twin Fidelity d) Digital Twins in Aerospace Engineering e) Case Studies on Digital Twins f) AI and Reasoning g) Health Management of Aerospace Systems Prognostics & Remaining Useful Life (RUL) of Aerospace Systems a) Probability of Failure & Weibull Statistics b) Prediction of RUL Utilising Physics-Based Models c) Prediction of RUL Utilising Data-Driven Models d) Prediction of RUL Utilising Knowledge-Based Models e) Hybrid Prognostic Methodology of Aerospace Systems f) Case Studies on Prognostic Health Management

Teaching methods include: •Lectures with worked examples •Tutorials and problem-solving workshops •Laboratory sessions on relevant topics to support and enhance the theoretical understanding Learning activities include: •Solving example problems, attending labs, participating in tutorials •Completion of coursework, and practical write-ups

Summative

This is how we’ll formally assess what you have learned in this module.