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Authors
Yang, Fan; Shah, Sirish; Xiao, Deyun

Digital Object Identifier (DOI)
10.3182/20100802-3-ZA-2014.00011

Page Numbers:
50-55

Index Terms
Fault diagnosis and process monitoring; Data mining and multivariate statistical analysis; Liquid and solid waste treatment

Abstract
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

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