Operational state monitoring of permanent magnet synchronous motor based on voiceprint recognition

DING Xiying; FU Zhigang; MA Shaohua

doi:10.7688/j.issn.1000-1646.2025.02.02

Abstract：[Objective]In the field of traditional permanent magnet motor fault monitoring,while contact signals are widely used,they usually only reflect one operational state of motors,leading to insufficient information and difficulty in comprehensively identifying the operational state of permanent magnet synchronous motors.To enrich the amount of information,additional sensors are needed,which not only increases the complexity of the system but is also difficult to be practically applied.Therefore,improving the accuracy and convenience of permanent magnet motor state monitoring has become an important research objective.With the development of intelligent monitoring technology,the application of non-contact signals has received increasing attention.The audio signals generated by the operation of permanent magnet motors contain rich state information,providing a new direction for fault diagnosis.Compared with contact signals,audio signals can reflect in real time such characteristics as motor vibration and noise caused by faults,which have significant research value.However,these signals are easily interfered by environmental noise,which results in poor signal quality and unclear feature information and is thereby not conducive to the state monitoring of permanent magnet synchronous motors.Therefore,a deep learning model based on voiceprint recognition was proposed for permanent magnet synchronous motors,aiming to efficiently monitor and diagnose operational states of motors through deep learning technology.[Methods]Firstly,the wavelet denoising algorithm was used to reduce noise interference,improve signal quality,and thus enhance the signal-to-noise ratio,ensuring that the model can more clearly extract Mel cepstral features and laying the foundation for fault identification and classification.However,direct use of convolutional neural networks(CNNs)to extract Mel cepstral features may weaken the correlation between features,affecting the accuracy of fault identification.To address this,a spatial attention mechanism was introduced,which enhanced the spatial position correlation of features through weighting,leading the model to focus on the most critical parts and thus improving the effectiveness of feature extraction.To boost the recognition accuracy of the model,normalization of Mel cepstral features was performed,and the AAM-softmax loss function was employed.This function strengthened inter-class constraints,improving the distinguishing capability of the model between different categories,thereby enhancing the recognition accuracy and generalization ability,and optimizing the training process,so that the model was enabled to better adapt to different operating conditions.[Results]Simulation test results indicate that the proposed model performs excellently on the training set,accurately identifying the different operational states of the motor,and demonstrates strong generalization ability on the test set.The experimental results confirm that the deep learning-based voiceprint recognition method can effectively monitor the various operational states of permanent magnet motors with high accuracy and practicality.[Conclusion]In summary,the proposed deep learning model based on voiceprint recognition for permanent magnet synchronous motors can effectively eliminate noise and extract key features.By introducing the spatial attention mechanism and the AAM-softmax loss function,the model significantly enhances the recognition accuracy and generalization ability.With broad prospects for development,this model can be widely applied in state monitoring and fault diagnosis of permanent magnet motors and promote the development of intelligent maintenance technology of motors.