Fault-Prognosis-Text-Mining.pdf (490.41 kB)
Automatic Recommendation of Prognosis Measures for Mechanical Components based on Massive Text Mining
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.