Title

Data Warehouse &Amp; Data Mining Techniques For Airframe Corrosion Control

Keywords

Aerospace; Corrosion control; Data mining; Data warehouse

Abstract

The Solid Rocket Boosters of the Space Shuttle have very unique and critical requirements as airframe structures. They support the entire shuttle stack, absorb stresses of main engine start, experience water impact at greater than 60 miles per hour, and remain immersed in salt water for approximately 48 hours. In addition, the maintenance and deployment of the hardware takes place in a seacoast environment. In order to maintain the integrity of the hardware, inspections are performed on the entire structure after each flight to locate corrosion for remediation and analysis. The data for these inspections has been kept graphically on paper and descriptively on standard spreadsheet software. This paper describes the development of a data warehouse that will be used to facilitate inspections through a graphical interface, store all the data in flexible format and allow the mining of the data for new information on the structure. Specifically, the software will be used to identify areas where the corrosion protection design is deficient, monitor the effect of changes to the corrosion protection system, and trend the damage accumulation for life prediction. This is the first phase of implementation of a data warehouse on the structures. Work is also in progress to allow the inclusion of maintenance history, problem reports, materials process and traceability data, and to support the structural analysis of the hardware. The ultimate driver of this effort is increased system reliability by the transformation of currently available data, using data mining techniques, into information, which can be used to make better resource management decisions.

Publication Date

1-1-1999

Publication Title

NACE - International Corrosion Conference Series

Volume

1999-April

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

Socpus ID

84877985787 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84877985787

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