Remaining Useful Life (RUL) prediction is critical for ensuring equipment safety and optimizing maintenance schedules, directly impacting system reliability and maintenance efficiency. However, in real-world industrial scenarios, factors such as operating condition fluctuations and load variations lead to inconsistent data distributions, making it challenging for existing models to achieve satisfactory adaptability and accuracy. To address this issue, this paper proposes a deep learning framework based on a multi-branch serial-parallel fusion of CNN-BiLSTM-Transformer architectures. Through innovative model architecture design and optimized training strategies, the framework aims to enhance... More >