@misc{12803,
  abstract     = {{The increasing amount of alarms and information for an operator in a modern plant becomes a significant safety risk. Although the notifications are a valuable support, they also lead to the curse of overloading with information for the operator. Due to the huge amount of alarms it is almost impossible to separate the crucial information from the insignificant ones. Therefore, new procedures are required to reduce these alarm floods and support the operator to minimize the safety risk. One approach is based on learning a causal model that represents the relationships between the alarms. This allows alarm sequences that are causally implied to be reduced to the root cause alarm. Fundamental element of this approach is the causal model. Therefore in this work, different probabilistic graphical models are considered and evaluated on the basis of appropriate criteria. A real use case of a bottle filling module serves as a benchmark for how well they are suitable as a causal model for the application in alarm flood reduction.}},
  author       = {{Wunderlich, Paul and Hranisavljevic, Nemanja}},
  booktitle    = {{2019 IEEE 17th International Conference on Industrial Informatics (INDIN)}},
  isbn         = {{978-1-7281-2928-0}},
  keywords     = {{probabilistic graphical model, causal model, alarm flood reduction, Bayesian network, Markov chain, restricted boltzmann machine, automata}},
  location     = {{Helsinki, Finland }},
  pages        = {{1285--1290}},
  publisher    = {{IEEE}},
  title        = {{{Comparison of Different Probabilistic Graphical Models as Causal Models in Alarm Flood Reduction}}},
  doi          = {{10.1109/indin41052.2019.8972251}},
  year         = {{2019}},
}

@inproceedings{4254,
  abstract     = {{The current trend of integrating machines and factories into cyber-physical systems (CPS) creates an enormous complexity for operators of such systems. Especially the search for the root cause of cascading failures becomes highly time-consuming. Within this paper, we address the question on how to help human users to better and faster understand root causes of such situations. We propose a concept of interactive alarm flood reduction and present the implementation of a first vertical prototype for such a system. We consider this prototype as a first artifact to be discussed by the research community and aim towards an incremental further development of the system in order to support humans in complex error situations.}},
  author       = {{Büttner, Sebastian and Wunderlich, Paul and Heinz, Mario and Niggemann, Oliver and Röcker, Carsten}},
  booktitle    = {{ Machine Learning and Knowledge Extraction : First IFIP TC 5, WG 8.4, 8.9, 12.9 International Cross-Domain Conference, CD-MAKE 2017, Reggio, Italy, August 29 – September 1, 2017, Proceedings}},
  editor       = {{Holzinger, Andreas}},
  isbn         = {{978-3-319-66807-9}},
  keywords     = {{Alarm flood reduction, Machine learning, Assistive system}},
  location     = {{Reggio, Italy}},
  pages        = {{69--82}},
  publisher    = {{Springer}},
  title        = {{{Managing Complexity: Towards Intelligent Error-Handling Assistance Trough Interactive Alarm Flood Reduction}}},
  volume       = {{10410}},
  year         = {{2017}},
}