Domain Adaptation Networks With Parameter-Free Adaptively Rectified Linear Units for Fault Diagnosis Under Variable Operating Conditions

As an important component of the rotating machinery, rolling bearings usually work under the condition of variable speed and load, and vibration signals in the same health state are significantly different due to the change in operating conditions. To address the problem that the existing deep learning (DL) methods have fixed nonlinear transformations for all input signals in cross-domain fault diagnosis, we propose a new activation function, i.e., parameter-free adaptively rectified linear units (PfAReLU). The proposed activation function performs adaptive nonlinear transformations according to the input data and can better capture the fault features of vibration signals in the same fault state under different operating conditions. Furthermore, the number of PfAReLU parameters is zero, so that the risk of network overfitting is reduced. At the same time, deep parameter-free reconstruction-classification networks with PfAReLU (DPRCN-PfAReLU) are also constructed for cross-domain fault diagnosis. Specifically, DPRCN-PfAReLU consists of a shared encoder, a target domain decoder, and a source domain classifier. The shared encoder adds a parameter-free attention module at the output to enhance the weight of domain-invariant features without increasing network parameters. The shared encoded representation of source domain and target domain is learned by target domain decoder and source domain classifier. Compared with other methods under nine different operating conditions via real experiment studies, the proposed method shows superiority for cross-domain fault diagnosis.

Automated summary

Rolling bearings are important components of rotating machinery and typically operate under variable speed and load conditions. Vibration signals in the same health state exhibit significant differences due to changes in operating conditions. To address fixed non-linear transformations in existing deep learning methods for cross-domain fault diagnosis, a new activation function called parameter-free adaptively rectified linear units (PfAReLU) is proposed. PfAReLU performs adaptive non-linear transformations based on input data and effectively captures fault features of vibration signals under different operating conditions. Furthermore, a deep parameter-free reconstruction-classification network with PfAReLU (DPRCN-PfAReLU) is constructed, which outperforms other methods for cross-domain fault diagnosis in real experiment studies under nine different operating conditions.