## 2 Literature Review  
### Data Augmentation Technology  
Data augmentation technology can be divided into traditional methods of simple integration or adjustment of data and methods based on deep learning. Traditional enhancement methods include algorithms for the transformation and amplification of the data themselves and fusion methods for multiple and heterogeneous types of data to fuse data from different sources. Conversion and amplification algorithms for the data themselves come from the field of image recognition, in which different types of conversion are performed on the data, such as clipping, scaling, or translation [22; 23; 24]. However, due to the particularity of time-series data distributions, such algorithms cannot be directly applied for the enhancement of time-series data. Improved basic data enhancement methods for time series include jitter, arrangement, etc. The jitter method is designed to take advantage of the noise in data and simulate it to generate new samples. [PERSON] et al. used the jitter method to enhance time-series data on wind power generation, thereby extending a sparse and unbalanced original dataset that recorded the working state of wind power equipment. Arrangement is performed to generate new data by specifying a time window and rearranging data from it [25]. [PERSON] et al. improved the arrangement method and proposed a dynamic time-warping algorithm to enhance test data on rolling bearings of electromechanical equipment [26]. However, the learning of jitter for noise requires one to adapt to a variety of situations; otherwise, it will lead to negative learning. The arrangement method has the problem of not retaining time dependence, which may cause invalid samples to be generated. Multi-source heterogeneous data fusion refers to the integration of data from multiple data sources, such as different devices, sensors, systems, and networks, to make up for the shortcomings of a single data source and obtain data with higher dimensions. [PERSON] et al. proposed a multi-source heterogeneous data fusion model based on a CatBoost feature layer and data layer, which fused sensor measurement data, offline inspection data, and video monitoring data from electromechanical equipment in a production line [27]. However, most of the fault data for platform screen door systems come from manual inspection, and the data sources are limited, so it is difficult to carry out effective heterogeneous data fusion. Data enhancement methods based on deep learning are mainly based on the variational autoencoder (VAE) and generative adversarial network (GAN). The variational autoencoder (VAE) is composed of two parts: an inference network and a generation network. It realizes the approximate reasoning of a complex distribution by minimizing the KL divergence between two distributions and realizes the generation of continuous and smooth data. [PERSON] et al. used the VAE to enhance fault detection data from semiconductor wafer manufacturing equipment and used the augmented data for classification learning [28]. In practical applications, the data distribution of time series is usually complex and contains a variety of internal associations. However, the VAE often assumes that the data distribution is a Gaussian distribution, which may lead to its inability to accurately capture the true distribution pattern of data in the process of data enhancement. The proposal of the generative adversarial network (GAN) solves the problem of it being difficult for generated data to simulate the real distribution pattern of the original data. [PERSON] et al. used a GAN architecture to generate data from two different types of fault data sequences. The generator and discriminator of each GAN were composed of many-to-many LSTM models, so the model was able to process the input time information [29]. [PERSON] et al.