Time Series Data Generation Method with High Reliability Based on ACGAN - Fang Liu; Yuxin Li; Yuanfang Zheng (Entropy)
In the process of big data processing, especially in fields like industrial fault diagnosis, there is often the issue of small sample sizes. The data generation method based on Generative Adversarial Networks(GANs) is an effective way to solve this problem. Most of the existing data generation methods do not consider temporal characteristics in order to reduce complexity. This can lead to insufficient feature extraction capability. At the same time, there is a high degree of overlap between the generated data due to the low category differentiation of the real data. This leads to a lower level of category differentiation and reliability of the generated data. To address these issues, a time series data generation method with High Reliability based on the ACGAN (HR-ACGAN) is proposed, applied to the field of industrial fault diagnosis. First, a Bi-directional Long Short-Term Memory (Bi-LSTM) network layer is introduced into the discriminator.It can fully learn the temporal characteristics of the time series data and avoid the insufficient feature extraction capability. Further, an improved training objective function is designed in the generator to avoid high overlap of generated data and enhance the reliability of generated data. Finally, two representative datasets from the industrial fault domain were selected to conduct a simulation analysis of the proposed method. The experimental results show that the proposed method can generate data with high similarity. The dataset expanded with the generated data achieves high classification accuracy, effectively mitigating the issue of dataset imbalance. The proposed HR-ACGAN method can provide effective technical support for practical applications such as fault diagnosis.