Excavator activity recognition is a crucial task with significant practical applications such as assessing production efficiency, injury prevention, and enabling intelligent control. However, implementing deep learning-based activity recognition methods presents considerable challenges due to the requirement of large training datasets. And sensor-based activity recognition demands more convenient and reliable sensors. This paper proposes a generalized data augmentation framework for excavator activity recognition. Main pump pressure and flow signals are generated using Deep Convolutional Conditional Generative Adversarial Networks (DC-CGAN). The proposed framework integrates Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) architectures for activity recognition. Results indicate that the framework outperforms traditional data augmentation methods regarding the quality of generated data and shallow and single deep networks regarding model accuracy and generalization. Additionally, the framework was implemented on three sizes of excavators and a wheel loader, demonstrating excellent versatility.