Multi-adversarial domain adaptation (MADA) techniques enable the effective transfer of knowledge across multiple domain classifiers and have shown significant potential in transfer learning-based fault diagnosis of rotating machinery. Nevertheless, their performance is highly dependent on the availability of abundant labelled source domain data, and their feature extraction capability degrades severely when there exists a substantial domain discrepancy between source and target distributions. This challenge is particularly acute in the fault diagnosis of closed-loop hydraulic pumps with incipient or hidden faults, where the risk of misclassification is considerably elevated. To overcome these limitations, this paper presents a novel transfer fault diagnosis framework that integrates a dynamic simulation model with an enhanced multi-adversarial domain adaptation network, termed DS-IMADA (Dynamic Simulation-Improved Multi-Adversarial Domain Adaptation). Specifically, a dynamic pressure simulation model is established to simulate representative fault scenarios of hydraulic pumps, providing synthetic labelled data in the source domain under various fault conditions. Subsequently, a deep convolutional cross-branch parallel architecture with embedded self-attention mechanisms is employed to strengthen domain-invariant feature extraction. Additionally, an improved semi-supervised multi-adversarial pre-adaptation strategy is proposed to mitigate the negative transfer effect and enhance domain alignment. Finally, comprehensive transfer diagnosis experiments using simulated signals as source domain data and real measured signals as target domain data are conducted, and the results validate the effectiveness and robustness of the proposed method.