# Summarization of Remaining Life Prediction Methods for Special Power Plants
## 2 Research Status at Home and Abroad
### State of Research on Dynamic Data-Driven RUL Prediction Methods  
With the development and application of sensors, storage, network transmission, and other technologies, the monitoring of the operation processes of mechanical equipment has produced a large amount of data. These can be used to accurately predict RUL through the mining of the degraded information hidden in the data. To date, such methods have achieved some excellent results and been used to develop excellent public data sets (such as the FEMTO data set [67], Cincinnati data set [68], Xi'an Jiao-tong University data set [69]), and Siemens Predictive Maintenance Software Equipment Predictive Analytics (EPA), etc.). In conclusion, data-driven RUL prediction methods mainly consist of feature extraction, health indicator construction, and RUL prediction. In obtaining accurate and reliable life prediction results, efficient feature extraction plays an important role. Data features are mainly divided into the time domain, frequency domain, and time-frequency domain. The time domain feature extraction method directly calculates the data's statistical features, such as the mean, variance, root mean square, skewness, kurtosis, cliff, entropy, etc. The frequency domain feature extraction method uses Fourier transform to transform the time domain data into the frequency domain and then uses spectrum analysis to obtain the statistical features in the frequency domain. The time-frequency domain feature extraction method uses Hilbert-Huang transform, wavelet transform, empirical mode decomposition, etc. These methods can be used to expand the time domain data to a two-dimensional space, obtain the instantaneous frequency and amplitude at any time, and finally, obtain the time-frequency domain features [70; 71; 72; 73]. The above feature extraction methods select the data from different perspectives, but the key features extracted for different types of mechanical equipment are still key problems for data-driven methods which have not been successfully solved. Health indicator construction is the quantitative expression of the health state of mechanical equipment, which can be divided into two categories according to physical significance, namely, physical health indicators and virtual health indicators. Physical health indicators refer to the time domain, frequency domain, and time-frequency domain characteristics of mechanical equipment, which have physical significance but poor monotonicity and a poor trend and are seriously affected by noise [74]. Virtual health indicators refer to a type of indicators representing the running state of mechanical equipment, being obtained from the fusion of multiple physical health indicators or sensor data, which have no physical significance but can reflect the degradation trend and achieve good results in terms of monotonicity, trends, and scale similarity [75]. The main research methods include deep learning, principal component analysis, T-distributed random neighbor embedding, locally linear embedding, and the restricted Boltzmann machine [28; 76]. Based on feature extraction and health indicator construction, representative methods of RUL prediction include convolutional neural networks, deep belief networks, recurrent neural networks, and transfer learning. Convolutional neural networks have good feature extraction and generalization abilities, and their overall architecture includes an input layer, a convolutional layer (excitation layer, pooling layer, and fully connected layer), and an output layer. The stacked convolutional layers of the network and the pooling layer form a feature extractor, and the fully connected layer acts as a classifier or predictor, constituting an end-to-end network model. The model's accuracy and robustness depend on the types of network layers, the network depth, the arrangement of various types of layers in the network, the features chosen for each layer, and the training data [77; 78].