Sequential Modeling and Knowledge Source Integration for Identifying the Structure of a Bayesian Network for Multistage Process Monitoring and Diagnosis

Partha Protim Mondal, Placid Matthew Ferreira, Shiv Gopal Kapoor, Patrick N. Bless

Research output: Contribution to journalArticlepeer-review


As a popular applied artificial intelligence tool, Bayesian networks are increasingly being used to model multistage manufacturing processes for fault diagnosis purposes. However, the major issue limiting the practical adoption of Bayesian networks is the difficulty of learning the network structure for large multistage processes. Traditionally, Bayesian network structures are learned either with the help of domain experts or by utilizing data-driven structure learning algorithms through trial and error. Both approaches have their limitations. On the one hand, the expert-driven approach is costly, time-consuming, cumbersome for large networks, and susceptible to errors in assessing probabilities; on the other hand, data-driven approaches suffer from noise, biases, and inadequacy of training data and often fail to capture the physical causal structure of the data. Therefore, in this article, we propose a Bayesian network structure learning approach where popular manufacturing knowledge sources like the failure mode and effect analysis (FMEA) and hierarchical variable ordering are used as structural priors to guide the data-driven structure learning process. In addition, to introduce modularity and flexibility into the learning process, we present a sequential modeling approach for structure learning so that large multistage networks can be learned stage by stage progressively. Furthermore, through simulation studies, we compare and analyze the performance of the knowledge source-based structurally biased networks in the context of multistage process fault diagnosis.

Original languageEnglish (US)
Article number011005
JournalJournal of Manufacturing Science and Engineering
Issue number1
StatePublished - Jan 1 2024


  • Bayesian networks
  • big data
  • failure mode and effect analysis (FMEA)
  • machine learning
  • modeling and simulation
  • monitoring and diagnostics
  • multistage manufacturing processes
  • process monitoring
  • root cause identification
  • sensing

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Mechanical Engineering
  • Computer Science Applications
  • Industrial and Manufacturing Engineering


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