The loose slipper fault diagnosis of variable-displacement pumps under time-varying operating conditions

•The proposed framework can achieve high diagnostic accuracy of variable-displacement pumps under time-varying operating conditions.•The model is trained under partial constant operating conditions to achieve fault diagnosis under time-varying operating conditions.•The multi-scale attention mechanism residual network (MSARN) is innovatively developed to extract multi-scale fault features.•The guidelines for the selection of constant and representative training conditions are provided. Variable-displacement pumps (VDPs) are widely used as core power components of high-pressure hydraulic systems due to their superior power density. The loose slipper fault is a typical failure type of the VDP, which can cause premature damage or even unexpected shutdown. The on-site VDPs usually work under time-varying operating conditions (TVOCs), and their pressure varies with the changing speed. These unique characteristics pose a new challenge to the fault diagnosis (FD) of the VDPs. This study introduces an innovative FD method named the multi-scale attention mechanism residual network (MSARN). The MSARN is equipped with three well-chosen components: multi-scale convolution module (MSCM), attention mechanism (AM), and residual connection. The integration of these three components facilitates the efficient extraction and fusion of meaningful multi-scale fault features, thereby preventing the overfitting problem. The experimental results obtained on the VDP validate the effectiveness and accuracy of the MSARN. The suggested method trains the model using data collected under partial constant and representative operating conditions, enabling the achievement of FD under TVOCs.