# Large Language Model Based Intelligent Fault Information Retrieval System for New Energy Vehicles
## 3 System Design of Intelligent Retrieval of Fault Information  
### The Logical Structure Design of the Fault Data Retrieval System  
#### The Integration of Fault Diagnosis Technology  
The Fault Tree Analysis (FTA) based on the Fault Tree Model and the Fault Diagnosis Expert System are two important branches in the field of fault diagnosis. Exploring their integration to meet system requirements.  
1. Fault Tree Analysis  
Fault Tree Analysis (FTA), also known as Causal Tree Analysis, combines the advantages of qualitative analysis, rule-based reasoning, and quantitative analysis, making it a simple yet effective analysis method. It models the fault object as a fault tree, identifyingand determining all possible patterns that could lead to the top-level fault, and is a powerful tool for reliability analysis.  
The qualitative analysis in FTA focuses on evaluating the importance of events, i.e., analyzing the extent to which each event influences the occurrence of the top-level fault, and based on this, preventive measures are formulated. In a fault tree model, the occurrence of the top-level event may be determined by all the underlying events. However, in practice, the occurrence of the top-level event is often caused by a subset of the underlying events (cut sets). When the removal of any event in the cut set results in the non-occurrence of the top-level event, the cut set is referred to as a minimal cut set. The identification of minimal cut sets in the fault tree reveals various patterns of top-level event occurrences, highlighting the core role of minimal cut sets in fault tree analysis.  
(2) Fault Diagnosis Expert System  
A fault diagnosis expert system is an integrated system that combines artificial intelligence technology, aiming to simulate human experts in system fault data retrieval and diagnosis. These systems mainly achieve accurate and rapid diagnosis of complex system faults by integrating components such as specialized knowledge bases, reasoning mechanisms, and user interfaces. By combining expert knowledge with computer technology, a fault diagnosis expert system is constructed. This system follows specific reasoning algorithms and facilitates communication between the user and the computer through a human-computer interaction interface. The user needs to answer the questions posed by the system, and the system performs logical reasoning based on these questions and answers, ultimately deriving and presenting the diagnostic conclusion.  
(3) Integration of Fault Tree Analysis and Fault Diagnosis Expert System  
Through comparative analysis of the diagnostic expert system and fault tree analysis, commonalities between them can be identified. First, the fault tree can be seen as a fault model within the fault diagnosis expert system. The reasoning process of the expert system and the logical structure of the fault tree have similar designs. Additionally, from the perspective of knowledge acquisition, the fault tree represents a standardized knowledge framework. Using this framework to construct the knowledge base of the diagnostic expert system not only clearly outlines the solution path to the diagnostic problem but also significantly reduces the difficulty of knowledge acquisition for the system.  
The knowledge base constructed based on fault tree analysis contains not only the rich experience and causal relationships between faults in NEVs but also preserves the unique hierarchical relationships between the conclusions in the fault tree. Transforming the fault tree into a knowledge structure that combines a knowledge graph framework with rules effectively supports reasoning and analysis in the fault diagnosis process. This method not only facilitates the acquisition and simplification of fault diagnosis knowledge but also improves the efficiency and accuracy of the diagnostic process, providing technical support for the fault data retrieval system in this research on NEVs.