Automatic recommendation of prognosis measures for mechanical components based on massive text mining

Autoren Jorge Martinez-Gil
Bernhard Freudenthaler
Thomas Natschläger
Editoren Maria Indrawan-Santiago
Ivan Luiz Salvadori
Matthias Steinbauer
Ismail Khalil
Gabriele Anderst-Kotsis
Titel Automatic recommendation of prognosis measures for mechanical components based on massive text mining
Buchtitel Proceedings of the 19th International Conference on Information Integration and Web-based Applications & Services (iiWAS 2017)
Typ in Konferenzband
Verlag ACM
ISBN 978-1-4503-5299-4
DOI 10.1145/3151759.3151774
Monat December
Jahr 2017
Seiten 32-39
SCCH ID# 17012
Abstract

Automatically providing suggestions for predicting the likely status of a mechanical component is a key challenge in a wide variety of industrial domains. Existing solutions based on ontological models have proven to be appropriate for fault diagnosis, but they fail when suggesting activities leading to a successful prognosis of mechanical components. The major reason is that fault prognosis is an activity that, unlike fault diagnosis, involves a lot of uncertainty and it is not always possible to envision a model for predicting possible faults. In this work, we propose a solution based on massive text mining for automatically suggesting prognosis activities concerning mechanical components. The great advantage of text mining is that it is possible to automatically analyze vast amounts of unstructured information in order to find strategies that have been successfully exploited, and formally or informally documented, in the past in any part of the world.