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.


    Access

    Download


    Export, share and cite



    Title :

    Bayesian Causal Network for Discrete Variables


    Additional title:

    Intelligent Control & Learning Systems


    Contributors:
    Wang, Jing (author) / Zhou, Jinglin (author) / Chen, Xiaolu (author)


    Publication date :

    2022-01-03


    Size :

    17 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English





    Causal Bayesian NetworkX

    Pacer, Michael D. | TIBKAT | 2015


    A causal Bayesian network approach for consumer product safety and risk assessment

    Hunte, Joshua L. / Neil, Martin / Fenton, Norman E. | Elsevier | 2021


    Learning a Causal Model from Household Survey Data by Using a Bayesian Belief Network

    Torres, Francisco J. / Huber, Manfred | Transportation Research Record | 2003



    Engineering optimization with discrete variables

    Huang, Min-Wei / Arora, Jasbir | AIAA | 1995