Rolling bearing faults are among the primary causes of breakdown in multistage centrifugal pump. A novel method of rolling bearings fault diagnosis based on variational mode decomposition is presented in this contribution. The rolling bearing fault signal calculating model of different location defect is established by failure mechanism analysis, and the simulation vibration signal of the proposed fault model is investigated by FFT and envelope analysis. A comparison has gone to evaluate the performance of bearing defect characteristic extraction for rolling bearings simulation signal by using VMD and EMD. The result of comparison verifies the VMD can accurately extract the principal mode of bearing fault signal, and it better than EMD in bearing defect characteristic extraction. The VMD is then applied to detect different location fault features for rolling bearings fault diagnosis via modeling simulation vibration signal and practical vibration signal. The analysis result of simulation and experiment proves that the proposed method can successfully diagnosis rolling bearings fault.
In recent years, intelligent fault diagnosis technology with the deep learning algorithm has been widely used in the manufacturing industry for substituting time-consuming human analysis method to enhance the efficiency of fault diagnosis. The rolling bearing as the connection between the rotor and support is the crucial component in rotating equipment. However, the working condition of the rolling bearing is under changing with complex operation demand, which will significantly degrade the performance of the intelligent fault diagnosis method. In this paper, a new deep transfer model based on Wasserstein distance guided multi-adversarial networks (WDMAN) is proposed to address this problem. The WDMAN model exploits complex feature space structures to enable the transfer of different data distributions based on multiple domain critic networks. The essence of our method is learning the shared feature representation by minimizing the Wasserstein distance between the source domain and target domain distribution in an adversarial training way. The experiment results demonstrate that our model outperforms the state-of-the-art methods on rolling bearing fault diagnosis under different working conditions. The t-distributed stochastic neighbor embedding (t-SNE) technology is used to visualize the learned domain invariant feature and investigate the transferability behind the great performance of our proposed model. INDEX TERMS Transfer learning, fault diagnosis, convolutional neural network, multi-adversarial networks.
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