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Details of Grant 

EPSRC Reference: GR/L16613/01
Title: RELIABILITY PREDICTION OF HELICOPTER TRANSMISSION SYSTEMS WITH BAYESIAN UPDATING BY HUMS
Principal Investigator: Strutt, Professor J
Other Investigators:
Irving, Professor P
Researcher Co-Investigators:
Project Partners:
Ministry of Defence (MOD) Pre Nexus Migration UK Civil Aviation Authority
Westland
Department: Sch of Industrial and Manufacturing Scie
Organisation: Cranfield University
Scheme: Standard Research (Pre-FEC)
Starts: 27 January 1997 Ends: 26 January 2000 Value (£): 141,660
EPSRC Research Topic Classifications:
Mech. & Fluid Power Transmiss. Safety & Reliability of Plant
EPSRC Industrial Sector Classifications:
Aerospace, Defence and Marine Energy
Related Grants:
Panel History:  
Summary on Grant Application Form
A high level of reliability of helicopter rotor transmission systems is critical to the safety of helicopters and there is now a growing requirement, in both civil and military sectors, to demonstrate, by means of quantitative risk/reliability analysis, that systems safety targets can be met. The project aims to develop models for improved prediction of the reliability of helicopter transmission systems together with methods by which information on mechanical system integrity, provided by Health and Usage Monitoring Systems, can be used to update and narrow uncertainties in system reliability. Stress Strength Interference Methods will be developed in which component stress is related to damage accumulation and strength to component damage tolerance. It is envisaged that the proposed models will provide a tool by which: (1) component and system reliability can be predicted from a knowledge of the various underlying processes of material degradation and damage accumulation and (2) physical variability, model uncertainty and parameter uncertainty can be modelled explicitly. Bayesian updating routines will be developed which will enable HUMS data to be used for updating system reliability. The models will provide a framework for reducing uncertainty in the predicted time of failure and provide an input into maintenance management and improved specifications for effective HUM systems.
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Organisation Website: http://www.cranfield.ac.uk