## 1 Introduction
To reduce the risk of biased opinions, we leverage the DL technique to explore the implicit fault information embedded in AE signals. Among all types of DL models, the generative adversarial network (GAN) has shown a remarkable capacity to perform distribution fitting. GAN is a powerful generative model that was first proposed by [PERSON] et al. in 2014 [20]. Unlike conventional neural networks, GAN implements generative modeling as a game between two separated networks: a Generator is trained to produce synthetic data that are close to the real data, while a Discriminator is trained to discriminate between the synthetic and real data. During this training process, the probability distribution of the real data can be learned by the Generator. There are many successful applications of GAN in the fault diagnosis field. Existing research mainly focuses on the problem of 'unbalanced data', i.e., the sample size of anomal data is much smaller than that of regular data [21; 22; 23; 24; 25; 26]. GAN is utilized to generate synthetic abnormal data to assist model training. These studies have demonstrated that GAN has an excellent ability to learn representative features from mechanical signals. [PERSON] et al. summarized the applications of GAN to anomaly detection in a number of fields [27]. It can be concluded that GAN is suitable for early fault detection (EFD) problems in two ways [28]: (1) it shows superiority in fitting the distribution of health signals; the Generator can be trained to learn rich and hierarchical information from the data characteristic of the normal operating state of the machine; thus, these abnormal data should be poorly reconstructed; (2) the Discriminator forms a health indicator (HI) to indicate the abnormal signals. Although GAN is a promising approach for EFD, it faces a well-known issue of unstable training. In contrast to traditional supervised networks, whose performance can be well reflected by the loss value, GAN consists of two networks' fighting' with each other, so the loss values of the two networks show a relationship of 'as one falls, the other rises'. The balance between the Generator and Discriminator is subtle. Therefore, it is difficult to determine when the Discriminator is well-trained, leading to the problem of instability. In order to improve the robustness of GAN in the EFD problem, we introduced a new ensemble technique referred to as the 'history-state ensemble' (HSE) [29] method. HSE assumes that neural networks can generate multiple local optima during the training history. Our previous experimental results [29] have demonstrated that these local optima are diverse, and their combination improves the accuracy and stability of the single network. The ensemble method is generally perceived to be time-consuming. However, the benefit of the HSE is that it does not require extra training costs to obtain multiple base models. We only need to record the historical training model weights that should be discarded after each model update when using the backpropagation algorithm to adjust the model. Therefore, it does not require a specially designed network architecture. These historical training model weights are denoted as 'history states' or base models. In a brief summary, the main contributions of this paper are as follows. 1. We proposed a novel architecture of GAN consisting of convolutional blocks and LSTM for EFD in the run-to-failure process. 2. A new ensembled health indicator (EHI) is constructed by integrating GAN and a novel ensemble technique called the HSE method. 3. A laboratory durability test of a roller bearing element monitored by the acoustic emission technique was carried out to evaluate the effectiveness of the proposed method.