Fault severity recognition in axial piston pumps using attention-based adversarial discriminative domain adaptation neural network

Abstract

Abstract Axial piston pumps are the ‘hearts’ of hydraulic systems whose fault recognition is necessary for the safety and reliability of hydraulic equipment. These pumps operate under different operating conditions and the fault recognition model trained at one operating point cannot be applicable at another operating point due to the problem of domain shifts. This paper proposes a transfer learning method for the fault severity recognition of axial piston pumps based on adversarial discriminative domain adaptation fused with a convolutional channel attention module. First, a convolutional neural network is pre-trained with labeled vibration data from the source domain, and a convolutional channel attention module is added to assign weights to different convolution kernels. Second, the trained source model is transferred to the target domain, and its parameters are updated by an adversarial training process between the labeled source data and the unlabeled target data. Finally, vibration data are collected from an axial piston pump at different fault levels under various operating conditions to validate the proposed method. Experimental results indicate that the proposed method achieves an average recognition accuracy of 98.3% and outperforms some other transfer learning methods by a large margin.