Probabilistic Modelling of Temporal Correlations in Industrial Alarm Data

  • Robert Irimias

Student thesis: Phd


It is crucial that modern industrial plants maintain a safe environment in which processes can run efficiently. To achieve this, processes are being monitored via sensors and associated alarms. Data obtained through the monitoring system is displayed to human operators who must predict, identify and respond accordingly to faults of any nature and magnitude. However, operators can be overwhelmed leading to dangerous situations. It is thus desirable to create numerical analysis tools to facilitate their work. In the first part of this thesis, we explore the breadth of methods available in current literature in an attempt to give a unified overview of industrial data analysis. Next, given their direct relevance to the operators, the focus is on modelling discrete alarm data. A Bayesian network model is learnt from real historical alarm data and its limitations regarding the lack of representation of time dependencies are proven empirically. To overcome said limitations, a novel dynamic Bayesian Alarm network for representing alarm data is developed. It has a parsimonious, causally independent CPD and models time dependencies using geometric distributions. A new way of interpreting and analysing same-time/instant causation alarms is introduced in the model. Its applications in key alarm detection, clustering, flood identifications and are demonstrated on both real and synthetic data. The synthetic alarm data is obtained via a new heuristic random walk based method. Additional synthetic data is generated using the dynamic Bayesian alarm network and statistically compared to real data.
Date of Award1 Aug 2020
Original languageEnglish
Awarding Institution
  • The University of Manchester
SupervisorJonathan Shapiro (Supervisor)


  • prediction
  • early detection
  • similarity measure
  • historical alarm data
  • Dynamic Bayesian Network
  • artificial data generation
  • alarm management
  • Bayesian Network
  • probabilistic model learning

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