###### Abstract
proposed an end-to-end noise-reducing mixed combined attention variational self-encoder method for the effective extraction of fault features submerged in noise, for accurate axial piston pump fault diagnosis even in noisy environments [7]. [PERSON] et al. introduced a multi-sensor information feature extraction method based on vibration intensity theory. The filtered pump outlet flow and pressure signals were converted into velocity and acceleration signals through a physical quantity transformation approach, which enhanced the information comprehensiveness and state assessment accuracy [8]. Deep learning is commonly employed in fault diagnosis and classification. Convolutional neural networks (CNN), as a representative approach, can automatically learn data features, thereby enhancing the accuracy and reliability of diagnostic algorithms. [PERSON] et al. integrated vibration and pressure signals from various pump health states into RGB images and used a CNN for recognition [9]. [PERSON] et al. proposed a fault diagnosis method based on the combination of a smooth pseudo-Wigner-Ville distribution and a CNN, which can effectively realize fault diagnosis for rolling bearings, identify the degree of performance degradation, and achieve high recognition accuracy [10]. [PERSON] et al. introduced a genetic approach to swiftly explore a set of potentially feasible one-dimensional convolutional neural network architectures, while simultaneously optimizing their hyperparameters. This methodology has been applied to fault detection in axial piston pumps [11]. To address the typical fault pattern recognition issue of axial piston pumps, [PERSON] et al. developed an adaptive convolutional neural network tailored to automatic fault classification, which demonstrated improved accuracy levels [12]. However, applying the above CNN heightens the complexity in the structure and tuning of parameters through training, such as the convolutional kernel weights, and demands a substantial amount of training data, resulting in high computational complexity. [PERSON] et al. employed random convolutional kernels to transform and classify time series data, enabling classifier training on datasets exceeding one million time series within approximately one hour [13]. [PERSON] et al. presented an anomaly detection method based on random convolution kernels for piston pumps [14]. [PERSON] et al. proposed a bearing performance degradation assessment model based on random convolution kernel transforms that enriched the characterization of bearing degradation trends by decomposing the VMD signals and extracting multidimensional sensitive features from the decomposed intrinsic mode functions (IMF) [15]. In the field of industrial production, abnormal or faulty equipment not only disrupts normal operation but also poses serious safety risks to personnel. Early warning via anomaly detection enables the identification of potential fault risks, prevents the occurrence of faults, and mitigates the losses resulting from faults. Therefore, anomaly detection in axial piston pumps is crucial. Although numerous scholars have focused on the fault diagnosis and condition monitoring of axial piston pumps, relatively little research has been conducted on anomaly detection. Therefore, in this study, we propose an axial piston pump anomaly detection method based on the outlet pressure signals of axial piston pumps, referred to as DTW-RCK-IF, which combines the dynamic time warping (DTW) algorithm for data segmentation, a random convolutional kernel (RCK) for feature extraction, and the isolation forest (IF) algorithm for anomaly detection. In this composite algorithm, the DTW algorithm is employed to partition the raw pressure pulsation signal, to preserve as much feature information as possible from the original data. Feature extraction is accomplished using a CNN with random convolutional kernels to enhance the feature diversity and comprehensiveness. Anomaly detection is performed using the isolation forest algorithm.