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.
Original language | English |
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Pages (from-to) | 102-115 |
Number of pages | 14 |
Journal | Journal of Manufacturing Systems |
Volume | 75 |
Early online date | 14 Jun 2024 |
DOIs | |
Publication status | Published - Aug 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.Keywords
- Root cause analysis
- Bayesian network
- Ensemble learning
- Product quality