Brushless dc motors (BLDCs) are widely used in various industrial fields. Failures in critical applications can lead to significant economic losses or even casualties, highlighting the importance of fault diagnosis research. BLDCs often operate under varying conditions, leading to the differences between the source and target domains used in data-driven models. While domain adaptation methods are commonly used to address this problem, they require access to the target domain during training, complicating model deployment. To overcome this, we propose the angular domain data mixed domain generalization network (ADMDG). This method leverages multiple source domains from different BLDC operating conditions to learn domain-generalized knowledge, enabling effective generalization to unseen target domains and allowing for a single training process to support multiple deployment scenarios. ADMDG employs an angular domain current resampling technique to convert timedomain currents into angular-domain currents, mitigating the impact of varying conditions. A convolutional neural network-based fault diagnosis model is constructed, and advanced data augmentation techniques, Mixup, are used to enhance model generalization. Extensive BLDC fault experiments demonstrate the superior domain generalization performance of the proposed method compared to other state-of-the-art approaches.