<p>Axial piston pump fault diagnosis plays a critical role in industrial application field. However, the existing methods face tremendous difficulties in disposing of multi-sensor data-driven and varied pressure pulsation issues. This makes it impossible to select effective diagnostic evidence from multi-sensor data, and the reserved diagnosis model trained under known pressure pulsation fails to adapt for new operation condition. Dedicated to these problems, this paper proposes the manifold transfer (MT) and ensemble filter strategy (EFS) for pump fault diagnosis. In this work, MT is constructed for dimension reduction and feature transformation based on curvilinear component analysis (CCA). It is capable of nonlinear manifold learning to address the issue of varied pressure pulsation. Then, an ensemble filter strategy with an information filtrate function is designed to improve the fault diagnosis performance. The effectiveness of the proposed method is validated by a fault experiment on axial piston pump. The experimental results demonstrate that compared with other existing methods, the proposed method is competitive in terms of diagnostic accuracy and efficiency. Highlights. The manifold transfer is proposed to solve the varied pressure pulsation issue. An ensemble filter strategy is devised to achieve accurate and efficient fault diagnosis without manual intervention. Axial piston pump fault simulation experiments are conducted to validate the effectiveness of proposed method.</p>