# 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
RUL prediction studies based on convolutional neural networks have the following characteristics [79; 80]:  
1. A convolutional neural network can process a large amount of raw data, which makes it suitable for large model computing processes. 2. Convolutional neural networks can realize automatic feature extraction and recognition without supervision and without human intervention. 3. A convolutional neural network has a strong processing ability with regard to mechanical signals, vibration signals, noise signals, optical signals, and other high-dimensional signals, and it has the function of automatic noise reduction during data processing. 4. A convolutional neural network has a relatively small number of parameters, which makes training more convenient and efficient and facilitates the construction of a deeper network structure. 5. A convolutional neural network is insufficient for data on time series feature extraction ability. Special power plants and other types of equipment with random degradation processes regarding the data information and characteristics of the equipment are often time-series-related, making it easy to lose important feature factors. 6. Convolutional neural network RUL prediction cannot quantitatively provide RUL uncertainty; thus, in this stage of the convolutional neural network, RUL prediction is often used in combination with other intelligent algorithms. The deep belief network is a joint distribution model combining observation data and labels which consists of multiple layers of restricted Boltzmann machines and one layer of a backpropagation neural network. The network is more expandable and can be used to perform unsupervised pre-training and supervised backpropagation on the data and, ultimately, obtain a good RUL mapping model [81; 82]. The RUL prediction studies based on deep belief networks have the following characteristics [83; 84]:  
1. A deep belief network can solve the training difficulty problems of the depth model. Through layer-by-layer pre-training and the reverse fine-tuning strategy, the feature representation and extraction of data ranging from shallow to deep can be realized, and the distributed features of the input data can be discovered. The use of this method in the time domain signal does not need to satisfy the requirements of periodicity. The information from data on deep-level feature extraction plays a greater role. 2. The short-term prediction performance of the deep belief network is better, while the long-term prediction performance is worse. 3. The RUL prediction results obtained with deep belief networks cannot reflect the uncertainty of the prediction results and generally need to be combined with other prediction methods to reflect the uncertainty of the prediction results. Recurrent neural networks have short-term memory; can be added to the gating mechanism to control information retention, discarding, and preservation in the memory unit; can learn the dependency relationship over a relatively long timespan; and can solve the phenomena of gradient disappearance, gradient explosion, etc. Typical recurrent neural networks are long short-term memory (LSTM) and gated recurrent unit (GRU) networks [85]. The study of RUL prediction based on recurrent neural networks has the following characteristics [86; 87]:  
1. In RUL prediction, recurrent neural networks are able to fuse the original learning samples with the updated learning model, which can retrain the samples and has the characteristics of a fast convergence speed and high stability while improving accuracy. 2. Recurrent neural networks have certain advantages in dealing with time-series data or before-and-after dependent degradation data but struggle to analyze and process multidimensional data. 3. Recurrent neural networks can effectively reduce the uncertainty caused by environmental perturbations through their unique gate structure. However, they can only realize the point estimation of RUL, and it is difficult to assess the confidence of the prediction results.