Equipment health state prediction question-answering system and method for pre-training large language model

Through pre-training large language models and template-prompted question-answering systems, the generalization and data missing problems in equipment health status prediction are solved, efficient and accurate equipment health status prediction and instant warning are achieved, and the stability and reliability of the production line are improved.

CN120821804APending Publication Date: 2025-10-21NORTHWESTERN POLYTECHNICAL UNIV
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Patent Information

Application Number
CN202510935160.3
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-07-08
Publication Date
2025-10-21

AI Technical Summary

Technical Problem

Existing deep learning equipment health status prediction methods have limitations in generalization, data missing and reasoning ability, especially in the case of very few samples or zero samples, the learning ability is poor, and traditional models are difficult to fully consider the complexity and diversity of equipment operating status, resulting in inaccurate prediction results.

Method used

By adopting a pre-trained large language model, combined with reversible instance normalization, embedding and tokenization, prompt learning-based reprogramming and template prompt question-answering system, the prediction of equipment health status and question-answering communication are realized through collection, labeling, editing and interaction modules.

Benefits of technology

It improves the accuracy and user-friendliness of equipment health status prediction, can process structured and unstructured data, provide immediate warning information, reduce maintenance costs, and improve the stability and reliability of production lines.

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Abstract

The invention discloses an equipment health state prediction question-answering system and method for pre-training a large language model. The system comprises an acquisition module, a marking module, an editing module, a prediction module and an interaction module. The acquisition module is used for acquiring time sequence data of industrial production equipment and preprocessing the time sequence data to obtain processed data; the marking module is used for dividing and marking the processed data to obtain a marked sequence; the editing module is used for reprogramming the mark sequence to obtain word representation of the time sequence data; the prediction module is used for collecting the health state of the industrial production equipment for prediction based on the word representation to obtain a prediction result; and the interaction module is used for interacting with the user to complete question and answer communication of the prediction result. According to the method, a large amount of structured and unstructured data can be processed, so that the key indexes and modes of the health state of the equipment are identified more accurately, the health state of the equipment is predicted, instant early warning information is provided, and an enterprise is helped to take measures before a fault occurs.
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