# Interactive and Intelligent Root Cause Analysis in Manufacturing with Causal Bayesian Networks and Knowledge Graphs

arXiv: https://arxiv.org/abs/2402.00043 · HTML: https://mlsanity.com/paper/2402.00043/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 32 references that are in the map.
- First posted on arXiv 20 Jan 2024.

## Builds on (most cited first, 9 of 9)

- [Knowledge Graphs](https://mlsanity.com/paper/2003.02320/) (2020)
- [DAG-GNN: DAG Structure Learning with Graph Neural Networks](https://mlsanity.com/paper/1904.10098/) (2019)
- [Learning Sparse Nonparametric DAGs](https://mlsanity.com/paper/1909.13189/) (2019)
- [CAM: Causal additive models, high-dimensional order search and penalized regression](https://mlsanity.com/paper/1310.1533/) (2013)
- [CIPCaD-Bench: Continuous Industrial Process datasets for benchmarking Causal Discovery methods](https://mlsanity.com/paper/2208.01529/) (2022)
- [Explainable Online Lane Change Predictions on a Digital Twin with a Layer Normalized LSTM and Layer-wise Relevance Propagation](https://mlsanity.com/paper/2204.01292/) (2022)
- [Causal Discovery for Manufacturing Domains](https://mlsanity.com/paper/1605.04056/) (2016)
- [Learning Causal Graphs in Manufacturing Domains using Structural Equation Models](https://mlsanity.com/paper/2210.14573/) (2022)
- [Root Cause Analysis in Lithium-Ion Battery Production with FMEA-Based Large-Scale Bayesian Network](https://mlsanity.com/paper/2006.03610/) (2020)

## Built on by (most cited first, 5 of 5)

- [Comprehensible Artificial Intelligence on Knowledge Graphs: A survey](https://mlsanity.com/paper/2404.03499/) (2024)
- [Knowledge graph enhanced retrieval-augmented generation for failure mode and effects analysis](https://mlsanity.com/paper/2406.18114/) (2024)
- [Anwendung von Causal-Discovery-Algorithmen zur Root-Cause-Analyse in der Fahrzeugmontage](https://mlsanity.com/paper/2407.16388/) (2024)
- [From Latent to Lucid: Transforming Knowledge Graph Embeddings into Interpretable Structures with KGEPrisma](https://mlsanity.com/paper/2406.01759/) (2024)
- [Root Cause Analysis Of Productivity Losses In Manufacturing Systems Utilizing Ensemble Machine Learning](https://mlsanity.com/paper/2407.21503/) (2024)

## Abstract (arXiv)

Root Cause Analysis (RCA) in the manufacturing of electric vehicles is the process of identifying fault causes. Traditionally, the RCA is conducted manually, relying on process expert knowledge. Meanwhile, sensor networks collect significant amounts of data in the manufacturing process. Using this data for RCA makes it more efficient. However, purely data-driven methods like Causal Bayesian Networks have problems scaling to large-scale, real-world manufacturing processes due to the vast amount of potential cause-effect relationships (CERs). Furthermore, purely data-driven methods have the potential to leave out already known CERs or to learn spurious CERs. The paper contributes by proposing an interactive and intelligent RCA tool that combines expert knowledge of an electric vehicle manufacturing process and a data-driven machine learning method. It uses reasoning over a large-scale Knowledge Graph of the manufacturing process while learning a Causal Bayesian Network. In addition, an Interactive User Interface enables a process expert to give feedback to the root cause graph by adding and removing information to the Knowledge Graph. The interactive and intelligent RCA tool reduces the learning time of the Causal Bayesian Network while decreasing the number of spurious CERs. Thus, the interactive and intelligent RCA tool closes the feedback loop between expert and machine learning method.