采用贝叶斯优化和多尺度卷积网络的五相永磁同步电机匝间短路诊断*

ITSC Fault Diagnosis for Five-phase Permanent Magnet Synchronous Motors Using Bayesian Optimization and Multiscale Convolutional Neural Network

  • 摘要: 传统神经网络算法虽成功应用于永磁电机的匝间短路故障诊断中,但其在噪声干扰下难以提取出足够的故障特征量,且依靠经验选取的模型超参数无法达到性能最优,限制了其应用范围。为此,提出了一种采用贝叶斯优化的多尺度卷积神经网络算法,并将其用于五相永磁同步电机早期ITSC故障诊断。首先,使用多尺度卷积神经网络采集足够的故障特征。其次,引入贝叶斯优化算法实现模型超参数全局寻优,节省调参时间。最后,该算法通过提取实际电流信号数据实现不同短路情况下的早期ITSC故障诊断。为了证明所提算法的优越性,将其与其他四种算法进行比较,结果表明所提方法的准确率更高。

     

    Abstract: Although the traditional neural network algorithm has been successfully applied to the inter-turn short circuit(ITSC) fault diagnosis of permanent magnet motors, it is difficult to extract enough fault features under the noise interference, and the model hyperparameters selected by relying on experience cannot reach the optimal performance, which restricts its wide application. Thus, a multiscale convolutional neural network algorithm using Bayesian optimization(BO) is proposed for early ITSC fault diagnosis of five-phase permanent magnet synchronous motors(FPPMSM). Firstly, sufficient fault features are collected using a multiscale convolutional neural network(MSCNN). Secondly, the Bayesian optimization algorithm is introduced to achieve global optimization of model hyperparameters and to save tuning time. Finally, the algorithm achieves early ITSC fault diagnosis in different short-circuit situations by extracting the actual current signal data. To prove the effectiveness of the proposed algorithm, it is compared with other four algorithms and the experimental results show that the proposed method is more accurate.

     

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