Industrial-Grade Smart Troubleshooting through Causal Technical Language Processing: a Proof of Concept
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909274157875200 |
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| author | Trilla, Alexandre Yiboe, Ossee Mijatovic, Nenad Vitrià, Jordi |
| author_facet | Trilla, Alexandre Yiboe, Ossee Mijatovic, Nenad Vitrià, Jordi |
| contents | This paper describes the development of a causal diagnosis approach for troubleshooting an industrial environment on the basis of the technical language expressed in Return on Experience records. The proposed method leverages the vectorized linguistic knowledge contained in the distributed representation of a Large Language Model, and the causal associations entailed by the embedded failure modes and mechanisms of the industrial assets. The paper presents the elementary but essential concepts of the solution, which is conceived as a causality-aware retrieval augmented generation system, and illustrates them experimentally on a real-world Predictive Maintenance setting. Finally, it discusses avenues of improvement for the maturity of the utilized causal technology to meet the robustness challenges of increasingly complex scenarios in the industry. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2407_20700 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Industrial-Grade Smart Troubleshooting through Causal Technical Language Processing: a Proof of Concept Trilla, Alexandre Yiboe, Ossee Mijatovic, Nenad Vitrià, Jordi Artificial Intelligence Computation and Language Machine Learning Methodology This paper describes the development of a causal diagnosis approach for troubleshooting an industrial environment on the basis of the technical language expressed in Return on Experience records. The proposed method leverages the vectorized linguistic knowledge contained in the distributed representation of a Large Language Model, and the causal associations entailed by the embedded failure modes and mechanisms of the industrial assets. The paper presents the elementary but essential concepts of the solution, which is conceived as a causality-aware retrieval augmented generation system, and illustrates them experimentally on a real-world Predictive Maintenance setting. Finally, it discusses avenues of improvement for the maturity of the utilized causal technology to meet the robustness challenges of increasingly complex scenarios in the industry. |
| title | Industrial-Grade Smart Troubleshooting through Causal Technical Language Processing: a Proof of Concept |
| topic | Artificial Intelligence Computation and Language Machine Learning Methodology |
| url | https://arxiv.org/abs/2407.20700 |