SDG (Signed Directed Graph) Based Process Description and Fault Propagation Analysis for a Tailings Pumping Process
Automation in Mining, Mineral and Metal Processing, Volume # 13 | Part# 1
Yang, Fan; Shah, Sirish; Xiao, Deyun
Digital Object Identifier (DOI)
Fault diagnosis and process monitoring; Data mining and multivariate statistical analysis; Liquid and solid waste treatment
Variables in a process are interacting, thus they can be described as an SDG in which arcs show causal relations between variables. Based on the SDG, the fault propagation can be tracked along consistent paths. Hence the SDG modeling can form the basis of fault propagation analysis. Regarding the modeling issue, this paper suggests a knowledge-based method to capture connectivity information between and within units from piping and instrumentation diagrams and other process knowledge. On the other hand, process data can be employed to construct SDGs by correlation analysis. An SDG generation procedure is proposed in this paper. The individual disadvantages of these two methods are summarized. However it is shown that they complement each other when combined. The SDG modeling and fault propagation analysis are applied to a tailings pumping process to illustrate and validate the methods proposed in this paper.
REFERENCES Bauer, M., Cox, J.W., Caveness, M. H., Downs, J.J., and Thornhill, N.F. (2007). Finding the direction of disturbance propagation in a chemical process using transfer entropy. IEEE Transactions on Control Systems Technology, 15(1), 12–21. Bauer M. and Thornhill, N.F. (2008). A practical method for identifying the propagation path of plant-wide disturbances. Journal of Process Control, 18(7–8), 707–719. Yang, F., Xiao, D., and Shah, S.L. (2010). Qualitative fault detection and hazard analysis based on signed directed graphs for large-scale complex systems. In Fault Detection, In-Tech, Vukovar, Croatia, 15–50. Tangirala, A.K. Shah, S.L., and Thornhill, N.F. (2005). PSCMAP: A new tool for plant-wide oscillation detection. Journal of Process Control, 15(8), 931–941. Iri, M., Aoki, K., O'shima, E., and Matsuyama, H. (1979). An algorithm for diagnosis of system failures in the chemical process. Computers & Chemical Engineering, 3(1-4), 489–493. Jiang, H., Patwardhan, R., and Shah, S.L. (2009). Root cause diagnosis of plant-wide oscillations using the adjacency matrix. Journal of Process Control, 19(8), 1347–1354. Maurya, M.R., Rengaswamy, R., and Venkatasubramanian, V. (2003). A systematic framework for the development and analysis of signed digraphs for chemical processes. 1. Algorithms and analysis. Industrial & Engineering Chemistry Research, 42(20), 4789–4810. Oyeleye, O.O. and Kramer, M.A. (1988). Qualitative simulation of chemical process systems: steady-state analysis. AIChE Journal, 34(9), 1441–1454. Thambirajah, J., Benabbas, L., Bauer, M., and Thornhill, N.F. (2009). Cause-and-effect analysis in chemical processes utilizing XML, plant connectivity and quantitative process history. Computers & Chemical Engineering, 33(2), 503–512.