## 1 Introduction
Although methods for generating one-dimensional spectra or two-dimensional images have been extensively explored, [PERSON] et al. proposed a feature generation network that creates one-dimensional feature sets rich in fault information, suitable for further fault identification [24]. Examples of GAN-based data augmentation for different signal types are shown in Table 1 below. Building on this foundation, our research has further evolved to incorporate adversarial sample augmentation techniques. This strategy, by introducing targeted adversarial perturbations directly into the data, creates adversarial samples for training. Compared to traditional GAN generation methods, adversarial sample augmentation not only enhances the model's sensitivity and detection capabilities for complex fault conditions but also improves its robustness against unknown or varying fault patterns. By simulating potential fault variations, our model is better equipped to adapt to the complex and dynamic conditions encountered in actual operations, thereby increasing the accuracy and reliability of fault diagnosis. Building on our previous discussion regarding the challenges and limitations of meta-learning in industrial fault diagnosis, the research framework provided by [PERSON] et al. [12] offers a systematic approach to thinking about few-shot learning. They have meticulously categorized few-shot learning from three perspectives: data, models and algorithms. Following this framework, our study introduces a comprehensive strategy combining data augmentation, model optimization and algorithmic improvements to overcome the existinglimitations of meta-learning techniques, particularly when dealing with complex industrial fault-diagnosis data. To address the challenges in fault diagnosis, our research integrates adversarial sample generation with serialized task learning to enhance the model's robustness and adaptability. This method leverages modular design and advanced data-augmentation strategies to enable effective management of diverse fault-diagnosis scenarios. Our approach includes generating adversarial samples to enhance the robustness of the model against varied fault conditions. This strategy not only increases the dataset's diversity but also sharpens the model's capability to recognize and respond to anomalies that are not typical of the training data, substantially improving its diagnostic accuracy under operational conditions. The model adopts a modular design that allows for greater structural flexibility, facilitating easy adjustments and optimizations tailored to specific fault-diagnosis tasks. This adaptability enhances the model's efficiency and accuracy in identifying faults, making it particularly suited to dynamic industrial environments. Employing serialized task learning refines the learning process by structuring the sequence of tasks from simple to complex. This strategic arrangement ensures efficient utilization of limited sample data for training, thereby enhancing the model's rapid adaptability to new fault types and maintaining performance stability across diverse industrial settings. Together, these elements substantially enhance the model's fault-diagnosis capabilities, providing a robust framework that significantly improves adaptability and accuracy compared to traditional methods. The integration of adversarial learning within the serialized task framework is particularly effective in preparing the model for the unpredictability of real-world operational conditions. The Adversarial Task Augmented Sequential Meta-Learning (ATASML) framework introduced in this study enhances diagnostic models by integrating adversarial examples within a meta-learning architecture, significantly improving generalization capabilities. Embedding adversarial tasks during the training phase, ATASML prepares the model to handle unexpected or novel fault scenarios, thus robustly boosting adaptability and accuracy--ideal for industrial settings with variable and complex sensor data.