Zhong, Shengtong5; Langseth, Helge5; Nielsen, Thomas Dyhre6
1 Machine Intelligence, The Faculty of Engineering and Science (ENG), Aalborg University, VBN2 Aalborg U Robotics, The Faculty of Humanities, Aalborg University, VBN3 Department of Computer Science, The Faculty of Engineering and Science (ENG), Aalborg University, VBN4 The Faculty of Engineering and Science (TECH), Aalborg University, VBN5 Department of Computerand Information Science, The Norwegian University of Science and Technology6 Distributed, Embedded and Intelligent Systems, The Faculty of Engineering and Science (ENG), Aalborg University, VBN
Monitoring a complex process often involves keeping an eye on hundreds or thousands of sensors to determine whether or not the process is stable. We have been working with dynamic data from an oil production facility in the North sea, where unstable situations should be identified as soon as possible. Motivated by this prob- lem setting, we propose a general model for classification in dynamic domains, and exemplify its use by showing how it can be employed for activity detection. We con- struct our model by using well known statistical techniques as building-blocks, and evaluate each step in the model-building process empirically. Exact inference in the proposed model is intractable, so in this paper we experiment with an approximate inference scheme.
Reliability Engineering and System Safety, 2014, Vol 121, p. 61-71