Data mining for lifetime prediction of metallic components

Authors:
Esther Ge;Richi Nayak;Yue Xu;Yuefeng Li
Affiliations:
Queensland University of Technology, Brisbane, Australia;Queensland University of Technology, Brisbane, Australia;Queensland University of Technology, Brisbane, Australia;Queensland University of Technology, Brisbane, Australia
Venue:
AusDM '06 Proceedings of the fifth Australasian conference on Data mining and analystics - Volume 61
Year:
2006

Citing 9
Cited 3

The nature of statistical learning theory

The nature of statistical learning theory
Bagging predictors

Machine Learning
From data mining to knowledge discovery: an overview

Advances in knowledge discovery and data mining
Bayesian networks for knowledge discovery

Advances in knowledge discovery and data mining
Case-Based Reasoning in Design

Case-Based Reasoning in Design
Induction of Decision Trees

Machine Learning
Neural network analysis of structural damage due to corrosion

ISUMA '95 Proceedings of the 3rd International Symposium on Uncertainty Modelling and Analysis
Editorial: introduction to operations research and data mining

Computers and Operations Research
Domain-Driven actionable knowledge discovery in the real world

PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining

CAKE - Classifying, Associating and Knowledge DiscovEry - An Approach for Distributed Data Mining (DDM) Using PArallel Data Mining Agents (PADMAs)

WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 03
A user driven data mining process model and learning system

DASFAA'08 Proceedings of the 13th international conference on Database systems for advanced applications
A comparative analysis of classification algorithms in data mining for accuracy, speed and robustness

Information Technology and Management

Quantified Score

Hi-index	0.00

Visualization

Abstract

The ability to accurately predict the lifetime of building components is crucial to optimizing building design, material selection and scheduling of required maintenance. This paper discusses a number of possible data mining methods that can be applied to do the lifetime prediction of metallic components and how different sources of service life information could be integrated to form the basis of the lifetime prediction model.