Air-cooled inverter failure prediction decision assistance system

By constructing a multi-dimensional sensing module and a hybrid intelligent prediction module, the problem of multi-factor coupling characteristics of electrical-heating-environmental factors in air-cooled frequency converters was solved, achieving high-precision fault prediction and intelligent decision-making, and improving operation and maintenance response efficiency.

CN122174146APending Publication Date: 2026-06-09DATANG HEBEI NEW ENERGY ZHANGBEI
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
DATANG HEBEI NEW ENERGY ZHANGBEI
Filing Date
2026-02-06
Publication Date
2026-06-09

AI Technical Summary

Technical Problem

Existing frequency converter fault monitoring systems fail to fully consider the multi-factor coupling characteristics of air-cooled frequency converters, including electrical, heat dissipation, and environmental factors, resulting in low prediction accuracy, a lack of intelligent decision-making capabilities, and low operation and maintenance response efficiency.

Method used

A multi-dimensional sensing module is constructed to collect data, which is then fused using weighted principal component analysis. This is combined with a hybrid intelligent prediction module for hierarchical prediction, generating fault classification results and providing customized operation and maintenance solutions.

Benefits of technology

It improves the accuracy of predicting specific faults of air-cooled frequency converters and the efficiency of operation and maintenance response, thereby reducing operation and maintenance costs.

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Abstract

The application discloses a forced air cooling frequency converter fault prediction decision assistance system and particularly relates to the technical field of frequency converter fault detection, which comprises a forced air cooling frequency converter multidimensional sensing module, a multidimensional fault feature extraction module, a hybrid intelligent prediction module, a forced air cooling frequency converter fault grading module, a forced air cooling frequency converter decision generation module and a forced air cooling frequency converter man-machine interaction module; the forced air cooling frequency converter multidimensional sensing module collects multidimensional sensing data of the forced air cooling frequency converter, including electrical data, heat dissipation data and environmental data, and performs preprocessing to obtain a multidimensional parameter set; the application constructs a hierarchical architecture of a first-level basic prediction, a second-level accurate prediction and a comprehensive fusion prediction, the first-level prediction reduces the calculation load, improves the early warning capability, the second-level prediction only focuses on abnormal samples, realizes the accurate output of probability, life and fault type, and the comprehensive prediction dynamically weights and fuses the results of each level, thereby improving the operation efficiency and the prediction accuracy.
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