Ensemble Bayesian Network for root cause analysis of product defects via learning from historical production data

Karen Wang, Chao Liu, Yuqian Lu*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Root Cause Analysis (RCA) of product defects is crucial to improving manufacturing quality and productivity. However, current efforts to localize root causes are prone to limitations in the aspect of robustness, causality discovery, knowledge representation, stochasticity, and sample size. Therefore, we propose a product-wise Ensemble Bayesian Network (EBN) to provide a robust, intelligent and human-interpretable probabilistic reasoning method for RCA. BN is adopted to enable interpretable probabilistic reasoning under uncertainty. We developed various structure learning algorithms, a parameter learning algorithm, and a Bayesian inference algorithm for BN to learn the root causes of product quality issues from historical product defect records. Our Ensemble Learning (EL) techniques enhance BN base learners with bootstrapped re-sampling and combine the predictions from multiple structure learning algorithms, ensuring a robust performance of BN. The framework structure is modularized by products to reduce the sample size and achieve high efficiency. We proved our method achieved good performance in acquiring causal knowledge, identifying the root cause with probabilities, and predicting quality risks in production, from implementation and extensive experimental testing on real-world data collected from the plastic industry.
Original languageEnglish
Pages (from-to)102-115
Number of pages14
JournalJournal of Manufacturing Systems
Early online date14 Jun 2024
Publication statusE-pub ahead of print - 14 Jun 2024

Bibliographical note

Copyright © 2024 The Author(s). Published by Elsevier Ltd on behalf of The Society of Manufacturing Engineers. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/).

Data Access Statement

The authors do not have permission to share data.


  • Root cause analysis
  • Bayesian network
  • Ensemble learning
  • Product quality


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