Abstract
In many real-world fault diagnosis applications, due to the frequent changes in working conditions, the distribution of labeled training data (source domain) is different from the distribution of the unlabeled test data (target domain), which leads to performance degradation. In order to solve this problem, an end-to-end unsupervised domain adaptation bear fault diagnosis model that combines Riemann metric correlation alignment and one-dimensional convolutional neural network (RMCA-1DCNN) is proposed in this study. Second-order statistic alignment of the specific activation layer in source and target domains is considered to be a regularization item and embedded in the deep convolutional neural network architecture to compensate for domain shift. Experimental results on the Case Western Reserve University motor bearing database demonstrate that the proposed method has strong fault-discriminative and domain-invariant capacity. Therefore, the proposed method can achieve higher diagnosis accuracy than that of other existing experimental methods.
Highlights
Rolling bearings are key components in heavy-duty machinery and manufacturing systems and have been widely used in modern industries
Li et al [21] presented a deep domain adaptation method for bearing fault diagnosis based on multikernel maximum mean discrepancies between domains in multiple layers to learn representations from the source domain applied to the target domain
Small convolutional kernels in the preceding layers are used to deepen the network for multilayer nonlinear mapping and preventing overfitting [17]. e parameters of 1DCNN are detailed in Table 2. e pooling type is max pooling, and the activation function is ReLU. e ADAM stochastic optimization algorithm is applied to train the model to minimize the loss function, and the learning rate is set as 1e − 3. e experiments are conducted using the TensorFlow toolbox of Google
Summary
Rolling bearings are key components in heavy-duty machinery and manufacturing systems and have been widely used in modern industries. Zhang et al [17] took raw vibration signals as inputs of a deep convolutional neural network with the wide first-layer kernel convolutional neural network (WDCNN) model and used adaptive batch normalization (AdaBN) as the algorithm of domain adaptation to realize fault diagnosis under different load conditions and noisy environments. Li et al [21] presented a deep domain adaptation method for bearing fault diagnosis based on multikernel maximum mean discrepancies between domains in multiple layers to learn representations from the source domain applied to the target domain.
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