Troubleshooting Tree Construction From Unstructured Equipment Text
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Solution Overview
Problem
Constructing troubleshooting trees for complex systems is a tedious and time-consuming process that requires multidisciplinary collaboration, often taking several months to ensure reliability and accuracy.
Innovation Solution
A method utilizing a pre-trained language model and retrieval augmented generation to automatically construct troubleshooting trees by segmenting unstructured text sources, embedding metadata, and generating nodes based on equipment components, faults, and repair recommendations, with a grounding process for verification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a comprehensive analysis involving multidisciplinary teams is conducted to ensure reliability, then the troubleshooting tree accuracy is improved, but the construction time increases significantly
Solution Approach 1:
The system performs preliminary extraction of troubleshooting information from unstructured text sources (manuals, documentation) before actual tree construction. The LLM pre-processes and structures the information, creating a ready-to-use foundation that eliminates the need for time-consuming manual analysis while maintaining accuracy
Solution Approach 2:
A pre-trained language model serves as an intermediary between unstructured text sources and the troubleshooting tree structure. The LLM automatically extracts, structures, and validates troubleshooting information, replacing the need for multidisciplinary team collaboration while preserving the quality of expert-derived content
2Reliability
If manual construction and iterative testing are performed, then the troubleshooting tree reliability is improved, but the productivity decreases
Solution Approach 1:
The system performs self-validation through automated testing and verification processes. The LLM-generated troubleshooting tree is automatically tested against the source documentation and validated for logical consistency, enabling the system to self-correct and ensure reliability without requiring multiple manual iteration cycles
Solution Approach 2:
The system transforms unstructured text parameters into structured tree parameters automatically. By changing the state of information from unstructured to structured format through LLM processing, the system achieves both high productivity and reliability in a single pass rather than through repeated manual iterations
Data Source
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AI summary
Systems and methods for constructing an equipment troubleshooting tree involve receiving an unstructured text source and using a parsing tool to segment it into sections that are associated with equipment components, units, or processes. If the parsing tool fails, a pre-trained language model is used to identify the sections using the extracted listing as content. Metadata, including section-specific text and page ranges, are obtained for each section. A non-static process enhances similarity matching, resulting in segmented data. An indexing process embeds this data, along with the metadata, into index nodes to improve retrieval efficiency. The pre-trained language model generates a catalog of false, causes, or repair recommendations, which are structured as index notes in the troubleshooting tree. In response to a user query, the constructed troubleshooting tree is generated and output after a grounding process cross-references it with the unstructured text to ensure accuracy.