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

Abstract

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.

Publication DOI: https://doi.org/10.1016/j.jmsy.2024.06.001
Divisions: College of Engineering & Physical Sciences > School of Engineering and Technology > Mechanical, Biomedical & Design
College of Engineering & Physical Sciences > Smart and Sustainable Manufacturing
Funding Information: This work was supported by Callaghan Innovation R&D Fellowship Grant, New Zealand. The authors would like to thank Aspect Productivity Technology colleagues, Bob Dedekind for sharing domain knowledge and arranging client visits, Chris Rauch for providing
Additional Information: 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/).
Uncontrolled Keywords: Root cause analysis,Bayesian network,Ensemble learning,Product quality
Publication ISSN: 1878-6642
Data Access Statement: The authors do not have permission to share data.
Last Modified: 30 Sep 2024 13:47
Date Deposited: 18 Jun 2024 15:58
Full Text Link:
Related URLs: https://www.sci ... 278612524001213 (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2024-08
Published Online Date: 2024-06-14
Accepted Date: 2024-06-03
Authors: Wang, Karen
Liu, Chao (ORCID Profile 0000-0001-7261-3832)
Lu, Yuqian

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