The purpose of this study is to automatically provide 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.
This work proposes a solution based on massive text mining for automatically suggesting prognosis activities concerning mechanical components.
The great advantage of text mining is that makes possible to automatically analyze vast amounts of unstructured information to find corrective strategies that have been successfully exploited, and formally or informally documented, in the past in any part of the world.
The authors would like to thank the anonymous reviewers for their insightful comments and suggestions. The research reported in this work has been carried out in the frame if the project PROSAM funded by the Austrian Research Promotion Agency (Project Number 845578) and by the Austrian Ministry for Transport, Innovation and Technology, the Federal Ministry of Science, Research and Economy, and the Province of Upper Austria in the frame of the COMET center Software Competence Center Hagenberg (SCCH).
Martinez-Gil, J., Freudenthaler, B. and Natschläger, T. (2018), "Automatic recommendation of prognosis measures for mechanical components based on massive text mining", International Journal of Web Information Systems, Vol. 14 No. 4, pp. 480-494. https://doi.org/10.1108/IJWIS-04-2018-0029Download as .RIS
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