# Summarization of Remaining Life Prediction Methods for Special Power Plants
## 3 Future Research Directions
### Dynamic Data-Driven RUL Prediction Method
#### 3.2.2 RUL Prediction Method under Small-Sample and Non-Equilibrium Data Conditions  
The dynamic data obtained using the soft sensor method of follow-up dynamic parameters are limited and pertain to small samples. At the same time, in the short term, the sample size of the dynamic data is much larger than that of the degenerate state, which contains unbalanced data. Therefore, the dynamic data of special power plants are characterized by a small sample size and non-equilibrium conditions. The following steps can be taken to model and analyze the RUL of special power plants based on small-sample and non-equilibrium dynamic data:  
1. Data preprocessing based on the GSA-IFCM method. The data obtained through measurements of the accompanying sensor group are periodic time series data containing noise interference, which need to be filtered and denoised according to the operating frequency before extracting the unit cycle sequence. Then, the actual dynamic data in the unit cycle can be reversed through the dynamic excitation transfer matrix. The Improved Fuzzy C-Means (IFCM) algorithm and Genetic Simulated Annealing (GSA) algorithm can be combined to extract the cluster center of the maximum value of the unit time series, and the t-Mean Square Value (t-MSV) method can be used to calculate the energy curve of the time series by setting a time window to determine the start and end points of the unit period time series in order to adaptively extract the unit period time series. A basic flowchart of the data preprocessing steps based on the GSA-IFCM method is shown in Figure 9.  
Figure 8: Schematic diagram of a new type of follow-up measurement method. Figure 7: Schematic diagram of traditional contact measurement method. Data enhancement based on the SAE-ACGAN method. On the basis of data preprocessing (1), a sparse auto-encoder (SAE) is combined with ACGANs based on auxiliary classifier generative adversarial networks (ACGANs). Thus, a data enhancement method based on a time-frequency diagram and SAE-ACGANs is formed, as shown in Figure 10. Firstly, the SAE is applied for the in-depth extraction of high-dimensional data features. Then, an SAE structure is added as the extractor of the image features in the ACGAN framework, the raw data are encoded as hidden variables, noise is added through the standard normal distribution, and this is then input into the generator in order to strengthen its ability to represent the hidden variables related to the image category and reduce the generator range required to learn the real sample feature space, so that the image category features can be effectively maintained in the model training and recognition stage to improve the discriminator performance. This data enhancement method can be used to amplify small-sample and disequilibrium data. (3) Construction of an RUL prediction model based on the RCNN-ABi-LSTM method. On the basis of data enhancement, the good feature extraction ability and generalization ability of convolutional neural networks (CNNs) are used to construct a residual convolutional neural network attentional bidirectional long- and short-term memory (RCNN-Abi-LSTM) network RUL prediction model for special power plants. When extracting depth feature vectors from multi-dimensional sensor signals via CNN, the residual network structure is used to solve the problem of gradient disappearance caused by the deep CNN network. In view of the sequential correlation of sensor data, bidirectional long- and short-term memory (Bi-LSTM) networks can be used to learn the dynamic changes in the features proposed by the CNN and capture the long-distance-related features in the time series. In addition, the attention mechanism is introduced to provide different  
Figure 10: Basic flowchart of data enhancement based on the SAE-ACGAN method. Figure 9: Basic flowchart of data preprocessing steps based on the GSA-IFCM method. probability weights into the Bi-LSTM implicit states so as to strengthen the expression of key information regarding RUL prediction.