Frequency converter degradation prediction method under extreme environment based on causal inference

By applying causal inference and feature fusion technology in extreme environments, combining historical and future environmental data to predict the future operating conditions and deterioration types of the inverter, the problem of insufficient prediction accuracy and reliability in the existing technology is solved, and higher prediction accuracy and recognition accuracy are achieved.

CN119719920AActive Publication Date: 2025-03-28NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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Patent Information

Application Number
CN202510229024.2
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-02-28
Publication Date
2025-03-28
Estimated Expiration
2045-02-28

AI Technical Summary

Technical Problem

In extreme environments, it is difficult for the prior art to accurately predict the deterioration state of the inverter, and traditional methods ignore the causal relationship between environmental factors, resulting in poor reliability of the prediction results.

Method used

The frequency converter deterioration prediction method based on causal inference is adopted. Through causal inference and feature fusion technology, the influence characteristics of the environment on the working conditions are extracted, and the future working conditions data are predicted through the Transformer module.

Benefits of technology

It significantly improves the prediction accuracy of the future operating condition data of the inverter and the identification accuracy of the deterioration type, providing more reliable support for the stable operation of the communication system in extreme environments.

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Abstract

The invention belongs to the technical field of electronic device degradation prediction and identification, and particularly discloses a causal inference-based frequency converter degradation prediction method in an extreme environment, which is characterized in that the influence of the environment on working conditions is decomposed into redundant, unique and collaborative influences through causal inference, and the degrees of various influences are quantified through mutual information, so that the prediction accuracy is improved. Specific influence and degree of each environmental factor on the working condition of the frequency converter are determined, and a new analysis view angle is provided for a complex relationship between the environment and the working condition in an extreme environment; according to the method, the time sequence features are inquired through the environment features for feature fusion, the fusion features are spliced with the time sequence features on the basis of the fusion features, the fusion features are further enhanced, the influence of the current environment on the working condition is effectively captured, the time sequence effect in the degradation process of the frequency converter is fully considered, and the frequency converter degradation efficiency is improved. Therefore, the prediction precision of future working condition data is remarkably improved, and reliable support is provided for stable operation of a communication system in extreme environments such as desert, island reef, high altitude and extremely cold.
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