基于声纹识别的永磁同步电机运行状态监测  

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

作　　者：丁惜瀛[1] 付直刚 马少华[1] DING Xiying;FU Zhigang;MA Shaohua(School of Electrical Engineering,Shenyang University of Technology,Shenyang 110870,Liaoning,China)

机构地区：[1]沈阳工业大学电气工程学院,辽宁沈阳110870

出　　处：《沈阳工业大学学报》2025年第2期145-151,共7页Journal of Shenyang University of Technology

基　　金：国家自然科学基金面上项目(51877139)。

摘　　要：【目的】在传统永磁电机故障监测领域,接触式信号虽被广泛使用,但通常只能反映电机的单一运行状态且信息量不足,难以识别永磁同步电机的全面运行状态。为丰富信息量,需要额外增加传感器,但这不仅增加了系统的复杂性,还提高了实际应用难度。因此,提高永磁电机状态监测的精度与便捷性成为重要的研究目标。随着智能化监测技术的发展,非接触式信号的应用越来越受到关注。永磁电机运行时产生的音频信号包含了丰富的状态信息,为故障诊断提供了新的方向。相较于接触式信号,音频信号能实时反映由故障引起的电机振动、噪声等特征,有较大的研究价值。然而这类信号易受环境噪声的干扰,导致信号质量差、特征信息不清晰,不利于永磁同步电机的状态监测。针对上述问题,提出了一种基于声纹识别的永磁同步电机深度学习模型,旨在通过深度学习技术高效地监测和诊断电机运行状态。【方法】采用小波去噪算法减少噪声干扰,提升信号质量,进而提升信噪比,确保模型能够更清晰地提取梅尔谱特征,为故障识别和分类奠定基础。然而,直接使用卷积神经网络提取梅尔谱特征可能会削弱特征间的关联性,影响故障识别的精度。引入空间注意力机制,通过加权增强特征的空间位置相关性,使模型关注最关键的部分,提高特征提取的有效性。为提升模型的识别准确率,对梅尔谱特征进行归一化处理,并采用AAM-softmax损失函数。该函数通过强化类间约束,提高模型在不同类别之间的区分能力,进而提升识别精度和泛化能力并优化训练过程,使模型更好地适应不同工况。【结果】仿真测试结果表明,所提出的模型在训练集上表现出色,能够准确识别电机的不同运行状态,并在测试集上展现出较强的泛化能力。实验结果证实,基于深度学习的声纹识别方法能够有效监[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 wa

关 键 词：电机监测 声纹识别 小波去噪 损失函数 空间注意力机制 永磁同步电机 

分 类 号：TM307[电气工程—电机]