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Artificial intelligence and mathematical models for intelligent management of aircraft data

Artificial intelligence and mathematical models for intelligent management of aircraft data
Artificial intelligence and mathematical models for intelligent management of aircraft data
Increasingly, large volumes of aircraft data are being recorded in an effort to adapt aircraft maintenance procedures from being time-based towards condition-based techniques. This study uses techniques of artificial intelligence and develops mathematical models to analyse this data to enable improvements to be made in aircraft management, affordability, availability, airworthiness and performance. In addition, it highlights the need to assess the integrity of data before further analysis and presents the benefits of fusing all relevant data sources together.
The research effort consists of three separate investigations that were undertaken and brought together in order to provide a unified set of methods aimed at providing a safe, reliable, effective and efficient overall procedure. The three investigations are:
1. The management of helicopter Health Usage Monitoring System (HUMS) Condition Indicators (CIs) and their analysis, using a number of techniques, including adaptive thresholds and clustering. These techniques were applied to millions of CI values from Chinook HUMS data.
2. The identification of fixed-wing turbojet engine performance degradation, using anomaly detection techniques, applied to thousands of in-service engine runs from Tornado aircraft.
3. The creation of models to identify unusual aircraft behaviour, such as uncommanded flight control movements. Two Chinook helicopter systems were modelled and the models were applied to over seven hundred in-service flights.
In each case, the existing techniques were directed toward a condition-based maintenance approach, giving improved detection and earlier warning of faults.
knight, Peter Robin
570db1a8-6860-4cda-a795-cf49eedc81e2
knight, Peter Robin
570db1a8-6860-4cda-a795-cf49eedc81e2
Chipperfield, Andrew
524269cd-5f30-4356-92d4-891c14c09340

knight, Peter Robin (2012) Artificial intelligence and mathematical models for intelligent management of aircraft data University of Southampton, Faculty of Engineering and the Environment, Doctoral Thesis , 253pp.

Record type: Thesis (Doctoral)

Abstract

Increasingly, large volumes of aircraft data are being recorded in an effort to adapt aircraft maintenance procedures from being time-based towards condition-based techniques. This study uses techniques of artificial intelligence and develops mathematical models to analyse this data to enable improvements to be made in aircraft management, affordability, availability, airworthiness and performance. In addition, it highlights the need to assess the integrity of data before further analysis and presents the benefits of fusing all relevant data sources together.
The research effort consists of three separate investigations that were undertaken and brought together in order to provide a unified set of methods aimed at providing a safe, reliable, effective and efficient overall procedure. The three investigations are:
1. The management of helicopter Health Usage Monitoring System (HUMS) Condition Indicators (CIs) and their analysis, using a number of techniques, including adaptive thresholds and clustering. These techniques were applied to millions of CI values from Chinook HUMS data.
2. The identification of fixed-wing turbojet engine performance degradation, using anomaly detection techniques, applied to thousands of in-service engine runs from Tornado aircraft.
3. The creation of models to identify unusual aircraft behaviour, such as uncommanded flight control movements. Two Chinook helicopter systems were modelled and the models were applied to over seven hundred in-service flights.
In each case, the existing techniques were directed toward a condition-based maintenance approach, giving improved detection and earlier warning of faults.

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More information

Published date: 1 October 2012
Organisations: University of Southampton, Faculty of Engineering and the Environment

Identifiers

Local EPrints ID: 355717
URI: http://eprints.soton.ac.uk/id/eprint/355717
PURE UUID: 640da8d3-5106-4d61-be23-09e89c1a34ba
ORCID for Andrew Chipperfield: ORCID iD orcid.org/0000-0002-3026-9890

Catalogue record

Date deposited: 12 Nov 2013 16:45
Last modified: 18 Jul 2017 03:44

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Contributors

Author: Peter Robin knight
Thesis advisor: Andrew Chipperfield ORCID iD

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