To address the issues of incomplete,imbalanced,and partially missing condition monitoring data from railway switch machine,a fault diagnosis method of the plunger pump in switch machines with an improved Generative Adversarial Network(GAN)is proposed in this paper.First,a Conditional Deep Convolutional Generative Adversarial Network(C-DCGAN)model is established,with one-dimensional convolutional layers designed to capture the timing and frequency-domain features of the plunger pump vibration signal.The generator and discriminator are optimized through game confrontation mechanism to enhance the model's generalization and fault feature extraction capability.Then,Two Time-scale Update Rule(TTUR)is added to solve the slow learning issue in discriminator regularization,improving the stability of model training.Finally,a case study using measured data provided by the Electrical Equipment Materials Company of a railway bureau is conducted to validate the effectiveness of the method.The results show that the JSD obtained from the generated samples are 0.190,0.235,0.240 and 0.185 under the four working conditions.When the normal-to-fault sample ratios are 100∶1,50∶1,20∶1 and 10∶1,respectively,the fault classification accuracies reach 91.24％,94.13％,94.96％and 96.08％,consecutively.The proposed method achieves better performance in handling imbalanced data,achieving high fault classification accuracy,thereby providing robust support for ensuring the safe operation of railways.
关键词
铁路道岔/转辙机柱塞泵/非平衡数据/故障诊断/C-DCGAN
Key words
Railway turnout/Plunger pump for railway switch machine/Imbalanced data/Fault diagnosis/C-DCGAN
维普
万方数据