Abstract: Addressing the issues of limited fault samples for piston pumps, weak fault signals under noise interference, and traditional deep learning’s heavy reliance on vast amounts of training data, we proposed a novel few-shot fault diagnosis approach for piston pumps based on model-agnostic meta-learning（MAML） with few samples. Firstly, the collected one-dimensional vibration signal was decomposed using improved complete ensemble empirical mode decomposition with adaptive noise（ICEEMDAN）, resulting in intrinsic mode function（IMF） components. Subsequently, sensitive components rich in fault information were selected to enhance the feature information within the vibration signal. Secondly, a multi-channel one-dimensional convolutional model was established, incorporating a channel interaction feature encoder equipped with an efficient channel attention mechanism. This design aimed to focus on the mutual fault information among different channels, thereby effectively extracting general diagnostic knowledge applicable to multiple diagnostic meta-tasks. Finally, the one-dimensional convolutional model served as the base model, which was trained through the MAML method to obtain optimal initial model parameters. Following this, the optimally initialized model could quickly adapt to new operating conditions with limited piston pump fault samples, thereby realizing few-shot fault diagnosis for piston pumps.The performance of the proposed model was validated using experimental data from piston pump tests.Experimental results demonstrate that the proposed method achieves an accuracy rate of over 90% across various diagnostic tasks under few-sample conditions.