Permanent magnet synchronous motor current sensor fault diagnosis method based on lightweight double-branch feature fusion neural network

By constructing an adaptive fusion gating mechanism using a lightweight bi-branch feature fusion neural network and a sandworm population-Bayesian optimization collaborative algorithm, we solved the problems of model complexity and manual parameter tuning dependence in the fault diagnosis of current sensors for permanent magnet synchronous motors, and achieved high-precision, real-time fault identification and diagnosis.

CN120993301APending Publication Date: 2025-11-21HEFEI UNIV OF TECH
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Patent Information

Application Number
CN202511332893.4
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-09-18
Publication Date
2025-11-21

AI Technical Summary

Technical Problem

Existing fault diagnosis methods for permanent magnet synchronous motor current sensors suffer from high model complexity and strong reliance on manual parameter tuning, making it difficult to achieve efficient and real-time fault identification and diagnosis.

Method used

A lightweight dual-branch feature fusion neural network is adopted, which combines time-domain and frequency-domain features. The model parameters are automatically tuned using the sandworm population-Bayesian optimization collaborative algorithm (SSA-BOC), and an adaptive fusion gating mechanism is constructed to achieve fault diagnosis.

Benefits of technology

It achieves high-precision fault diagnosis of current sensors, reduces model complexity, and improves the accuracy and real-time performance of fault identification, making it suitable for embedded controllers and edge computing terminals.

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Abstract

The invention relates to the technical field of permanent magnet synchronous motor current sensor fault diagnosis, in particular to a permanent magnet synchronous motor current sensor fault diagnosis method based on a lightweight double-branch feature fusion neural network. The method comprises the steps of obtaining and preprocessing motor current data, constructing a current sensor fault diagnosis model, optimizing the current sensor fault diagnosis model, and performing fault diagnosis by using the current sensor fault diagnosis model. The method can realize high-accuracy fault identification in a small-sample and multi-working-condition environment, has the advantages of low model complexity, fast reasoning speed and strong edge deployment capability, is suitable for online fault diagnosis and predictive maintenance of the current sensor in the permanent magnet synchronous motor driving system, can monitor the health condition of the motor in real time, and has a good application prospect. And the service life of the motor can be prolonged.
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