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Interactive and Intelligent Root Cause Analysis in Manufacturing with Causal Bayesian Networks and Knowledge Graphs
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DOI：10.1109/VTC2023-Spring57618.2023.10199563.png)
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Abstract
En 中文
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 (CER's). Furthermore, purely data-driven methods have the potential to leave out already known CER's or to learn spurious CER's. 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 CER's. Thus, the interactive and intelligent RCA tool closes the feedback loop between expert and machine learning method.
Keywords:
Root Cause Analysis
Sensor Networks
Electric Vehicles
Interpretable Machine Learning
Interactive Learning
Bayesian Network
Knowledge Graph
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