=Paper=
{{Paper
|id=Vol-3828/ISWC2024_paper_45
|storemode=property
|title=Causal Neuro-Symbolic AI for Root Cause Analysis in Smart
Manufacturing
|pdfUrl=https://ceur-ws.org/Vol-3828/paper45.pdf
|volume=Vol-3828
|authors=Utkarshani Jaimini,Cory Henson,Amit Sheth,Ramy Harik
|dblpUrl=https://dblp.org/rec/conf/semweb/JaiminiHSH24
}}
==Causal Neuro-Symbolic AI for Root Cause Analysis in Smart
Manufacturing==
https://ceur-ws.org/Vol-3828/paper45.pdf
Causal Neuro-Symbolic AI for Root Cause Analysis in
Smart Manufacturing
Utkarshani Jaimini1 , Cory Henson2 , Amit Sheth1 and Ramy Harik3
1
Artificial Intelligence Institute, University of South Carolina, Columbia, SC, USA
2
Bosch Center for Artificial Intelligence, Pittsburgh, PA, USA
3
McNAIR Aerospace Center, University of South Carolina, Columbia, SC, USA
Abstract
Root cause analysis is the process of investigating the cause of a failure and providing measures to prevent
future failures. It is an active area of research due to the complexities in manufacturing production lines
and the vast amount of data that requires manual inspection. We present a combined approach of causal
neuro-symbolic AI for root cause analysis to identify failures in smart manufacturing production lines.
We have used data from an industry-grade rocket assembly line and a simulation package to demonstrate
the effectiveness and relevance of our approach.
Keywords
Causality, neuro-symbolic AI, root cause analysis, smart manufacturing
1. Root Cause Analysis in Smart Manufacturing
Smart Manufacturing, or Industry 4.0, represents a wave of innovations and new technologies
that are shaping the future of manufacturing with the goal of achieving more efficient production
lines. Advanced sensors and Internet of Things devices collect data about various aspects of
the production process, marking the onset of data-driven manufacturing. The collected data
is utilized for predictive maintenance, process optimization, and root cause analysis (RCA).
Traditional RCA is a costly and time-consuming process that involves manual inspection by
domain experts, potentially leading to production delays. The automation of RCA is an active
research area in smart manufacturing, aimed at minimizing downtime and ensuring cost-
effective production lines. Current work on RCA leverages ontologies, knowledge graphs (KG),
and neuro-symbolic methods to store expert knowledge, model production line dependencies,
and conduct reasoning to identify the time, location, and cause of failures [1]. In order to
better understand and explain the root cause of failures and provide preventive measures, it is
important to comprehend and model the causal associations in the data. However, parametric
causal AI approaches for RCA do not consider prior knowledge about the relationships and
parameters in the data [2]. Furthermore, traditional causal association approaches, which predict
causal relations between variables, do not scale well for large volumes of data [3]. Effective
Posters, Demos, and Industry Tracks at ISWC 2024, November 13–15, 2024, Baltimore, USA
$ ujaimini@email.sc.edu (U. Jaimini); cory.henson@us.bosch.com (C. Henson); amit@sc.edu (A. Sheth);
harik@mailbox.sc.edu (R. Harik)
0000-0002-1168-0684 (U. Jaimini); 0000-0003-3875-3705 (C. Henson); 0000-0002-0021-5293 (A. Sheth)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
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RCA necessitates the integration of causal associations (via causal models) among the data and
knowledge from diverse sources, offering enhanced explainability, scalability, and the ability to
perform intervention and counterfactual analysis.
2. Causal Neuro-Symbolic AI for Root Cause Analysis
Causal Neuro-Symbolic AI (NSAI) is a hybrid framework that blends the strengths of causal and
NSAI representations and techniques [4]. Our Causal NSAI-enhanced RCA approach supports
RCA by expressing the causal association among the data into symbolic representation. A KG
that encodes causal Bayesian network (CBN) based representation explicitly expresses the causal
association. The framework utilizes NSAI methods such as KG link prediction, providing better
scalability for inferring causal relations within large volumes of data. Causal NSAI provides the
following benefits: 1) Explainability- it enhances explanations for failures by combining causality
with symbolic (i.e., ontology and KG) reasoning and neural networks; 2) Robustness- it leverages
causal associations to improve the robustness of AI models to changes in data distribution,
and out-of-distribution data; 3) Generalization- it enhances generalizability by incorporating
prior domain knowledge with causal associations; 4) Intervention- it leads to models which
can predict the impact of interventions in the production line and make informed decisions to
achieve desired outcomes. This integration of causal associations with NSAI enables dynamic
adaptation to new manufacturing environments. The framework is applied to an industry-
grade dataset for a rocket assembly line1 and the causalAssembly2 based simulated dataset
[5]. The rocket assembly is a multi-modal data setup with four robots, rocket parts, conveyor
belts, a material handling station, stoppers, image and video recording, and sensors such as
temperature, potentiometer, load cells, robot angles, programmable logic controls, etc. A failure
in the assembly is defined as the absence of a rocket part in the final product. The Causal NSAI
framework is utilized to 1) provide explanations for the cause of the failure, 2) suggest measures
that can be taken to obtain a final product despite the failure, and 3) identify interventions and
counterfactuals using the causal associations to prevent future failures. Acknowledgments:
NSF Awards #2335967 and #2119654.
References
[1] T. Strobel, Root cause analysis for manufacturing using semantic web technologies, 2023.
[2] K. Budhathoki, L. Minorics, P. Blöbaum, D. Janzing, Causal structure-based root cause
analysis of outliers, in: International Conference on Machine Learning, PMLR, 2022.
[3] C. Glymour, K. Zhang, P. Spirtes, Review of causal discovery methods based on graphical
models, Frontiers in genetics 10 (2019) 524.
[4] U. Jaimini, C. Henson, A. Sheth, Causal neuro-symbolic ai: A synergy between causality
and neuro-symbolic methods, IEEE Intelligent Systems 39 (2024).
[5] R. Harik, F. Kalach, et al., Analog and multi-modal manufacturing datasets acquired on the
future factories platform, arXiv preprint arXiv:2401.15544 (2024).
1
Our public data is at https://www.kaggle.com/datasets/ramyharik/ff-2023-12-12-analog-dataset
2
https://github.com/boschresearch/causalAssembly