Fault early warning method and system based on AI large model

Through the fault warning method based on AI large model, multi-source heterogeneous data processing and neural network modeling are used to solve the problem of insufficient recognition capabilities in the existing technology under the changing scenarios of fault types, and high-precision fault warning and risk assessment are achieved.

CN120372485APending Publication Date: 2025-07-25LONGKUN (WUXI) SMART TECH CO LTD +1
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Patent Information

Application Number
CN202510462870.9
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-04-14
Publication Date
2025-07-25

AI Technical Summary

Technical Problem

When the prior art faces scenes with varying fault types, the recognition capability is insufficient and it is difficult to achieve high-precision fault warning.

Method used

Using the fault warning method based on AI large model, by acquiring multi-source heterogeneous data, denoising and standardizing the processing, the feature vector is extracted using the CNN neural network, combined with the K-means algorithm to identify the dynamic operating mode, the LSTM neural network is used to model the time series feature evolution, and the fault analysis is performed in combination with the Transformer structure to generate the early warning signal.

Benefits of technology

It significantly improves the prospective and robustness of fault warning, can accurately identify the precursors of faults in complex scenarios, and provides explainable risk assessment and parameter regulation suggestions.

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Abstract

The invention discloses a fault early warning method and system based on an AI large model, and the method comprises the steps: obtaining multi-source heterogeneous data, and carrying out the denoising and standardization processing, and obtaining fusion data; inputting into a feature extraction model, and outputting a feature vector set; identifying the dynamic operation mode based on a K-means algorithm to obtain an operation mode baseline; inputting a feature sequence model, and outputting a precursor feature sequence; calculating an abnormal score according to the precursor feature sequence, marking as abnormal if the score is greater than or equal to a threshold value, otherwise, marking as normal, and obtaining an abnormal detection result; evaluating a risk level according to a detection result; inputting the risk level into a fault analysis model to obtain fault cause distribution; determining optimized operation mode parameters according to the fault cause distribution; and performing deviation analysis on the data and the optimized parameters, and if a deviation value is greater than a threshold value, triggering an early warning signal. The method can solve the problem of insufficient recognition capability in a scene with variable fault types.
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