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2012 | OriginalPaper | Chapter

9. Condition Monitoring with Incomplete Information

Author : Tshilidzi Marwala

Published in: Condition Monitoring Using Computational Intelligence Methods

Publisher: Springer London

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Abstract

This chapter introduces a method for fault classification in mechanical systems in the presence of missing data entries. The method is based on auto-associative neural networks where the network is trained to recall the input data through some non-linear neural network mapping. An error equation with missing inputs as design variables is constructed from the trained network. The genetic algorithm was used to solve for the missing input values. The presented method is tested on a fault classification problem for a population of cylindrical shells. It was found that the method could estimate single-missing-entries to an accuracy of 93% and two-missing-entries to an accuracy of 91%. The estimated values were then used in the classification of faults and a fault classification accuracy of 94% was observed for single-missing-entry cases and 91% for two-missing-entry cases while the full database set gave a classification accuracy of 96%.

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Title: Condition Monitoring with Incomplete Information
Author: Tshilidzi Marwala
Publisher: Springer London
Book: Condition Monitoring Using Computational Intelligence Methods
Print ISBN: 978-1-4471-2379-8

Electronic ISBN: 978-1-4471-2380-4

Copyright Year: 2012
DOI: https://doi.org/10.1007/978-1-4471-2380-4_9

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