###### Abstract  
Axial piston pumps are critical components of hydraulic systems due to their compact design and high volumetric efficiency, making them widely used. However, they are prone to failure in harsh environments characterized by high pressure and heavy loads over extended periods. Therefore, detecting abnormal behavior in axial piston pumps is of significant importance. Traditional detection methods often rely on vibration signals from the pump casings; however, these signals are susceptible to external environmental interference. In contrast, pressure signals exhibit greater stability. In this study, we propose a novel anomaly detection method for axial piston pumps, referred to as DTW-RCK-IF, which combines dynamic time warping (DTW) for data segmentation, a random convolutional kernel (RCK) for feature extraction, and isolation forest (IF) for anomaly detection using pressure signals. The model is trained using normal operating data to enable the effective detection of abnormal states. First, the DTW algorithm is employed to segment the raw data, ensuring a high degree of similarity between the segmented data. Next, the random convolutional kernel approach is used in a convolutional neural network for feature extraction, resulting in features that are representative of normal operating conditions. Finally, the isolation forest algorithm calculates the anomaly scores for anomaly detection. Experimental simulations on axial piston pumps demonstrate that, compared with vibration signals, the DTW-RCK-IF approach using pressure signals yields superior results in detecting abnormal data, with an average F1 score of 98.79% and a good fault warning effect. Validation using the publicly available CWRU-bearing and XJTU-SY-bearing full-life datasets further confirms the effectiveness of this method, with average F1 scores of 99.35% and 99.73%, respectively, highlighting its broad applicability and potential for widespread use. axial piston pump; pressure signal; dynamic time warping; random convolution kernel; isolation forest; anomaly detection +
Footnote †: journal: Applied
combined with support vector machine techniques, which effectively extracted the fault characteristics from plunger pumps exhibiting non-linear and non-smooth behaviors [3]. [PERSON] et al. introduced a fuzzy entropy-assisted singular spectrum decomposition denoising approach for the detection of bearing faults in axial piston pumps [4]. [PERSON] et al. developed a probabilistic intelligent soft-state discrimination method using Levy flying quantum particle swarm-optimized multiclassified correlation vector machines with an improved fitness function designed explicitly for axial piston pumps [5]. [PERSON] et al. proposed a composite fault diagnosis method for axial piston pumps, combining Gram-angle difference fields with deep residual networks, which addressed the diagnosis of complex faults with different manifestations in various components of axial piston pumps [6]. However, vibration signals are susceptible to interference from the external environment and the installation of vibration sensors is complicated because of space limitations. In contrast, pressure signals are less susceptible to external environmental factors and other disturbances, resulting in improved stability and a direct reflection of the operational status of the axial piston pump. In addition, the installation process for pressure sensors is relatively simple. When abnormal conditions occur, the pressure signal exhibits more pronounced deviations, such as variations in the pulsation frequency and abrupt pressure changes, making these anomalous features easier to detect and analyze. Therefore, in the real-time monitoring, anomaly detection, and fault diagnosis of axial piston pumps, pressure signals are considered a better choice. In recent years, numerous studies have been conducted on fault diagnosis methods that utilize the pressure signals of axial piston pumps. [PERSON] et al.