Abstract
Process knowledge is the most reliable resource for qualitative modeling of complex industrial processes, which is typically expressed in natural language and stored in human brains. We thus need to capture useful connectivity and causality from such resources and convert the information into computer accessible formats. From first-principle structural models, causality can be captured and expressed as structural equations. From unstructured process knowledge and dynamic and algebraic equations, graphical models, in particular signed directed graphs and variants, can be obtained. Graphic models are widely used due to their computer tractability and human readability. Rule-based models are another alternative, which is used in expert systems. When the process information is accessible in web language, connectivity can be retrieved by query.
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Alonso CJ, Llamas C, Maestro JA, Pulido B (2003) Diagnosis of dynamic systems: a knowledge model that allows tracking the system during the diagnosis process. Lect Notes Artif Intell 2718(6):208–218
Bauer M, Cox JW, Caveness MH, Downs JJ, Thornhill NF (2007) Finding the direction of disturbance propagation in a chemical process using transfer entropy. IEEE Trans Control Syst Technol 15(1):12–21
Chang CC, Yu CC (1990) On-line fault diagnosis using the signed directed graph. Ind Eng Chem Res 29(7):1290–1299
Cheng H, Tikkala VM, Zakharov A, Myller T, Jamsa-Jounela SL (2011) Application of the enhanced dynamic causal digraph method on a three-layer board machine. IEEE Trans Control Syst Technol 19(3):644–655
Di Geronimo Gil GJ, Alabi DB, Iyun OE, Thornhill NF (2011) Merging process models and plant topology. In: Proceedings of 4th advanced control of industrial processes, Hangzhou, China
Fagarasan I, Ploix S, Gentil S (2004) Causal fault detection and isolation based on a set-membership approach. Automatica 40(12):2099–2110
Fedai M, Drath R (2005) CAEX—a neutral data exchange format for engineering data. ATP Int Autom Technol 3(1):43–51
Friston KJ, Harrison L, Penny W (2003) Dynamic causal modelling. NeuroImage 19(4):1273–1302
Gao D, Wu C, Zhang B, Ma X (2010) Signed directed graph and qualitative trend analysis based fault diagnosis in chemical industry. Chinese J Chem Eng 18(2):265–276
Jan A, Jonas B, Erik F, Krysander M, Lars N (2007) Safety analysis of autonomous systems by extended fault tree analysis. Int J Adapt Control Signal Process 21(2–3):287–298
Kramer MA, Palowitch BL Jr (1987) A rule-based approach to fault diagnosis using the signed directed graph. AIChE J 33(7):1067–1078
Leyval L, Gentil S, Feray-Beaumont S (1994) Model based causal reasoning for process supervision. Automatica 30(8):1295–1306
Mah RSH (1989) Chemical process structures and information flows. Butterworth, Boston, MA
Maurya MR, Rengaswamy R, Venkatasubramanian V (2003) A systematic framework for the development and analysis of signed digraphs for chemical processes. 1. Algorithms and analysis. Ind Eng Chem Res 42(20):4789–4810
Maurya MR, Rengaswamy R, Venkatasubramanian V (2003) A systematic framework for the development and analysis of signed digraphs for chemical processes. 2. Control loops and flowsheet analysis. Ind Eng Chem Res 42(20):4811–4827
Maurya MR, Rengaswamy R, Venkatasubramanian V (2007) A signed directed graph and qualitative trend analysis-based framework for incipient fault. Chem Eng Res Des 85(10):1407–1422
Montmain J, Gentil S (2000) Dynamic causal model diagnostic reasoning for online technical process supervision. Automatica 36(8):1137–1152
Mosterman PJ, Biswas G (1999) Diagnosis of continuous valued systems in transient operating regions. EEE Trans Syst Man Cybern Part A 29(6):554–565
Oyeleye OO, Kramer MA (1988) Qualitative simulation of chemical process systems: steady-state analysis. AIChE J 34(9):1441–1454
Pastor J, Lafon M, Trave-Massuyes L, Demonet JF, Doyon B, Celsis P (2000) Information processing in large-scale cerebral networks: the causal connectivity approach. Biol Cybern 82(1):49–59
Paynter HM (1960) Analysis and design of engineering systems. MIT Press, Cambridge, MA
Pearl J (2009) Causality: models, reasoning, and inference, 2nd edn. Cambridge University Press, Cambridge, UK
Shiozaki J, Matsuyama H, O’Shima E, Iri M (1985) An improved algorithm for diagnosis of system failures in the chemical process. Comput Chem Eng 9(3):285–293
Thambirajah J, Benabbas L, Bauer M, Thornhill NF (2009) Cause-and-effect analysis in chemical processes utilizing XML, plant connectivity and quantitative process history. Comput Chem Eng 33(2):503–512
Wright S (1921) Correlation and causation. J Agric Res 20:557–585
Yang F, Xiao D (2005) Approach to modeling of qualitative SDG model in large-scale complex systems. Control Instrum Chem Ind 32(5):8–11
Yang F, Xiao D (2006) Approach to fault diagnosis using SDG based on fault revealing time. Proceedings of 6th world congress on intelligent control and automation, Dalian, China, pp 5744–5747
Yang F, Shah SL, Xiao D (2009) SDG model-based analysis of fault propagation in control systems. Proceedings of 2009 Canadian conference on electrical and computer engineering, St John’s, NL, Canada, pp 1152–1157
Yang F, Shah SL, Xiao D (2012) Signed directed graph based modeling and its validation from process knowledge and process data. Int J Appl Math Comput Sci 22(1):41–53
Yang F, Xiao D, Shah SL (2013) Signed directed graph-based hierarchical modelling and fault propagation analysis for large-scale systems. IET Control Theory Appl 7(4):537–550
Yim SY, Ananthakumar HG, Benabbas L, Horch A, Drath R, Thornhill NF (2006) Using process topology in plant-wide control loop performance assessment. Comput Chem Eng 31(2):86–99
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Yang, F., Duan, P., Shah, S.L., Chen, T. (2014). Capturing Connectivity and Causality from Process Knowledge. In: Capturing Connectivity and Causality in Complex Industrial Processes. SpringerBriefs in Applied Sciences and Technology. Springer, Cham. https://doi.org/10.1007/978-3-319-05380-6_4
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DOI: https://doi.org/10.1007/978-3-319-05380-6_4
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