Over the last several decades, various machine learning methods, including artificial neural network [
3], support vector machine [
4], extreme learning machine [
5], XGBoost [
6], and
k-nearest neighbor, and decision trees, have been applied for the fault diagnosis of axial piston pumps [
7]. A common drawback of these traditional machine learning methods lies in the manual feature extraction. Therefore, in recent years, deep learning has become popular in the field of fault diagnosis of rotating machinery because of its powerful end-to-end ability. Many researchers have also found applications of deep learning methods in the fault diagnosis of axial piston pumps, such as deep belief network [
8] and one-dimensional (1D) and two-dimensional (2D) convolutional neural networks (CNNs) [
9–
15]. Among these previous studies, the vibration signal is most frequently selected to monitor pump health conditions [
4–
14] because it contain abundant fault information. Meanwhile, the discharge pressure signal appears to be suitable for monitoring the performance of axial piston pumps [
3,
16–
18].