|Title:||A classification framework for predicting components' remaining useful life based on discrete-event diagnostic data|
|Authors :||Fink, Olga|
|Published in :||IEEE transactions on reliability|
|Publisher / Ed. Institution :||IEEE|
|License (according to publishing contract) :||Licence according to publishing contract|
|Type of review:||Peer review (publication)|
|Subject (DDC) :||004: Computer science|
|Abstract:||In this paper, we propose to define the problem of predicting the remaining useful life of a component as a binary classification task. This approach is particularly useful for problems in which the evolution of the system condition is described by a combination of a large number of discrete-event diagnostic data, and for which alternative approaches are either not applicable, or are only applicable with significant limitations or with a large computational burden. The proposed approach is demonstrated with a case study of real discrete-event data for predicting the occurrence of railway operation disruptions. For the classification task, Extreme Learning Machine (ELM) has been chosen because of its good generalization ability, computational efficiency, and low requirements on parameter tuning.|
|Departement:||School of Engineering|
|Organisational Unit:||Institute of Data Analysis and Process Design (IDP)|
|Publication type:||Article in scientific journal|
|Appears in Collections:||Publikationen School of Engineering|
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