---
res:
  bibo_abstract:
  - In the presented work, the detection of anomalous energy consumption in hybrid
    industrial production systems is investigated. A model-based approach with a timed
    hybrid automaton as overall system model is employed for anomaly detection. The
    approach is based on the assumption of several system modes, i.e. phases with
    continuous system behavior. Transitions between the modes are attributed to discrete
    control events such as on/off signals. The underlying discrete event system which
    comprises both system modes and transitions is modeled as finite state machine.
    The focus of this paper is set on the modeling of the energy consumption in the
    particular system modes. Sequences of stochastic state space models are employed
    for this purpose. Model learning and anomaly detection for this approach are considered.
    The proposed approach is further evaluated in a small model factory. The experimental
    results show significant improvements compared to existing approaches to anomaly
    detection in hybrid industrial systems.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Stefan
      foaf_name: Windmann, Stefan
      foaf_surname: Windmann
      foaf_workInfoHomepage: http://www.librecat.org/personId=58525
  - foaf_Person:
      foaf_givenName: Shuo
      foaf_name: Jiao, Shuo
      foaf_surname: Jiao
  - foaf_Person:
      foaf_givenName: Oliver
      foaf_name: Niggemann, Oliver
      foaf_surname: Niggemann
      foaf_workInfoHomepage: http://www.librecat.org/personId=10876
  - foaf_Person:
      foaf_givenName: Holger
      foaf_name: Borcherding, Holger
      foaf_surname: Borcherding
      foaf_workInfoHomepage: http://www.librecat.org/personId=1693
  dct_date: 2013^xs_gYear
  dct_language: eng
  dct_publisher: IEEE@
  dct_title: A Stochastic Method for the Detection of Anomalous Energy Consumption
    in Hybrid Industrial Systems@
...