Ensuring the safety of industrial systems requires not only detecting the faults, but also locating them so that they can be eliminated. The previous chapters have discussed the fault detection and identification methods. Fault traceability is also an important issue in industrial system. This chapter and Chap. 14 aim at the fault inference and root tracking based on the probabilistic graphical model. This model explores the internal linkages of system variables quantitatively and qualitatively, so it avoids the bottleneck of multivariate statistical model without clear mechanism. The exacted features or principle components of multivariate statistical model are linear or nonlinear combinations of system variables and have not any physical meaning. So the multivariate statistical model is good at fault detection and identification, but not at fault root tracking.
Bayesian Causal Network for Discrete Variables
Intelligent Control & Learning Systems
Data-Driven Fault Detection and Reasoning for Industrial Monitoring ; Chapter : 13 ; 233-249
2022-01-03
17 pages
Article/Chapter (Book)
Electronic Resource
English
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