Abstract
This chapter proposes a framework for Situation Awareness (SA) of complex processes using data mining techniques and discrete event models. Referring to the well-known Endsley's model of SA the chapter presents the different stages of the framework and shows how this framework can be used for failure detection purposes. The chapter also provides some automatic tools enhancing the operator abilities to achieve SA to determine how well a complex system is functioning or to detect a failure for effective actions. Data mining techniques are applied to extract information from historical data records issued from process variables. The observation space is defined according to the selection of the most representative variables called descriptors, which allow the best characterization of the process functional states. The instantaneous functional state is described by a set of measurable values of these descriptors. © 2007
| Original language | English |
|---|---|
| Title of host publication | Fault Detection, Supervision and Safety of Technical Processes 2006 |
| Publisher | Elsevier |
| Pages | 1288-1293 |
| Number of pages | 6 |
| Volume | 2 |
| ISBN (Print) | 9780080444857 |
| DOIs | |
| Publication status | Published - 2007 |
| Externally published | Yes |
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