Cage damage in rolling bearings, due to its characteristics of abrupt faults and rapid degradation, is highly to result in bad accidents. This paper tries to solve the key challenge in incipient cage fault detection: how to capture the subtle abnormal changes of system states as early as possible in a noisy environment. A dynamic model-assisted multi-domain rule adaptation anomaly detection method (MD-RAAD) is proposed for extracting sensitive features. The core of this method is to integrate normal-state simulation data generated by the dynamic model of the target bearing into the anomaly detection transfer learning framework. First, a parameter inversion model is developed to correct the dynamic model so as to obtain simulation data that is more consistent with the actual data distribution. Second, a novel multi-domain adversarial neural network with rule adaptation mechanism is proposed to integrate auxiliary bearing data, target bearing data, and simulation data. Guided by the “noise-free” simulation data, the network adaptively extracts domain-invariant features that are more sensitive to incipient faults. The explicit rule adaptation constraint enables effective transfer at the detection rule level, which will enhance the discriminative capability and robustness of feature representations. Results on public bearing datasets demonstrate that the proposed method can find incipient fault occurrence in an earlier location with a lower false alarm rate.