Computers and Chemical Engineering 29 (2005) 1996–2009 Applications of object-oriented Bayesian networks for condition monitoring, root cause analysis and decision support on operation of complex continuous processes  G. Weidl a,∗ , A.L. Madsen b , S. Israelson c a Institute for Systems Theory in Engineering, University of Stuttgart, Pfaffenwaldring 9, 70550 Stuttgart, Germany b Hugin Expert A/S, Gasværksvej 5, DK-9000 Aalborg, Denmark c ABB Group Services, Forskargr¨ and 8, S-721 78 V ¨ aster˚ as, Sweden Received 1 May 2004; received in revised form 11 May 2005; accepted 19 May 2005 Available online 19 July 2005 Abstract The increasing complexity of large-scale industrial processes and the struggle for cost reduction and higher profitability means automated systems for processes diagnosis in plant operation and maintenance are required. We have developed a methodology to address this issue and have designed a prototype system on which this methodology has been applied. The methodology integrates decision-theoretic troubleshooting with risk assessment for industrial process control. It is applied to a pulp digesting and screening process. The process is modeled using generic object-oriented Bayesian networks (OOBNs). The system performs reasoning under uncertainty and presents to users corrective actions, with explanations of the root causes. The system records users’ actions with associated cases and the BN models are prepared to perform sequential learning to increase its performance in diagnostics and advice. © 2005 Elsevier Ltd. All rights reserved. PACS: Data Analysis, algorithms for, 0.7.05.K Keywords: Dynamic process disturbance analysis; Fault diagnostics, Pulp and paper 1. Introduction In large-scale and complex industrial processes, a fail- ure of the equipment or abnormality in process operation is usually detected by means of hardware sensors. The process operator has to isolate the cause of a failure by analyzing many sensors’ signals. The time until the failure source is identified and subsequently eliminated results in unplanned production interruption, which is the main source of cost increase due to lost production profit. The sheer amount of  Big part of this work has been performed while G.Weidl was associated with ABB Corporate Research, Sweden. ∗ Corresponding author. Tel.: +49 703 168 4970; fax: +49 711 685 7735. E-mail addresses: galia.weidl@ist.uni-stuttgart.de (G. Weidl), anders.l.madsen@hugin.com (A.L. Madsen), stefan.israelson@se.abb.com (S. Israelson). data and the continuity of the process require a high level of automation of operation and maintenance control, as well as advice to the human operator on corrective actions. In general, a fault diagnosis system for industrial process operation should satisfy the following requirements listed in (Vedam, Dash, & Venkatasubramanian, 1999 and Dash & Venkatasubramanian, 2000): early detection and diagno- sis; isolability; robustness; novelty identifiably; multiple fault identifiability; explanation facility; adaptability; reasonable storage and computational requirements. In this paper, we focus on key aspects of process monitor- ing and root cause analysis. In this context, Box and Kramer (1990) have discussed the roles of statistical/automatic process control for process monitoring/regulation. If only a classification of the failure type is required, neural networks or statistical classifiers may be more adequate. However, if decision support is needed, Bayesian networks 0098-1354/$ – see front matter © 2005 Elsevier Ltd. All rights reserved. doi:10.1016/j.compchemeng.2005.05.005