Optimisation of Fusion and Decision Making Techniques for Affordable SPHM

Azzam, H.; Wallace, Manolis; Beaven, F.; Hebden, I.

doi:10.1109/aero.2006.1656093

Cited by 1 publication

(3 citation statements)

References 5 publications

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“…It was found that the accuracy achieved previously was further improved [9]: the differences from the BAE SYSTEMS accumulative fatigue after 15 years were improved to 1.11%, 2.41%, -1.11% and 3.57% for Wing 1, Wing 2, Fin and Taileron respectively. For these airframe locations, the network accuracies were better than those of a strain gauge system with 1% error.…”

Section: Figure 11 -Hypercube Parameter Spacementioning

confidence: 87%

“…In order to address these objectives, legacy data were used to configure, optimise and test a suite of FUMS TM tools [5] to [9]. The Smiths FUMS TM tools included data quality algorithms, MNs that fuse flight data into prognostic information, dynamic event models, UIs, signal processing tools, AI tools and force life management software that have enabled an efficient application of these tools on large datasets.…”

Section: The Fums Tm Olm Techniquesmentioning

confidence: 99%

“…Smiths demonstrated that a wide range of dynamic events could be identified from measured flight parameters and that their high frequency effects could be simulated from flight parameters sampled at low rates. Whilst [5] to [9] present detailed descriptions of these tools and their validation results, this paper concentrates on MNs optimisation using Genetic Algorithms (GAs) [10] and on training data selection.…”

Section: The Fums Tm Olm Techniquesmentioning

confidence: 99%

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FUMS Technologies for Advanced Structural PHM

Azzam

Beaven

Smith

et al. 2007

2007 IEEE Aerospace Conference

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Over the past seven years, Smiths and BAE SYSTEMS have launched collaborative work to evolve a certifiable practical Structural Prognostic Health Management (SPHM) system. The collaborative work has built on BAE SYSTEMS' vast advanced technology experience and on Smiths' unique experience that has produced intelligent Fleet and Usage Management Software (FUMS TM ) including fusion, prognostic and decision support algorithms combining model-based and Artificial Intelligence (AI) techniques. This paper describes the recent advances and optimisation of the Smiths algorithms for damage detection and Operational Load Monitoring (OLM). A combination of FUMS TM signal processing and AI techniques have been applied to acoustic emission sensor data to locate and classify damage of different types in composite and metallic structures. The FUMS TM damage detection software has been embedded in real-time hardware to support ground tests. Techniques have been implemented to enable adequate calibration of OLM algorithms using data from flight tests. The techniques should address concerns raised about the accuracy of algorithms trained to synthesise strains throughout the entire flight envelope from data recorded close to the edge of the flight envelope. Working with the UK MOD, Smiths has continued the evaluation of FUMS TM software that allows aircraft design authorities and military operators to build their force life management applications without the need for software rewriting. 1,2

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Section: Figure 11 -Hypercube Parameter Spacementioning

confidence: 87%

Section: The Fums Tm Olm Techniquesmentioning

confidence: 99%