# Prognostics and Health Management Based on Next-Generation Technologies: A Literature Review
## Materials and Methods
### Analysis of the Data  
To comprehensively analyze the research and development of this topic, we used 3032 documents retrieved from three databases. We analyzed them in detail in terms of the number of articles per year and the frequency of keywords. The trend of the number of annual articles from the literature retrieved from the three databases was counted separately, as shown in Figure 3.  
An analysis of the number of articles in each database reveals that the growth trend of the number of articles in the three databases is roughly the same. From the figure, we can find that the number of related research topics has gradually increased since 2000. The first surge was generated in 2006, which may have been due to the explosive rise of deep learning at that time, and a variety of advanced deep learning models emerged, providing more and better avenues for PHM research. Then, as we entered the era of Industry 4.0, the rapid development of technologies, including digital twins and augmented reality, once again provided more possibilities for PHM research. As a result, a second surge in the number of publications of related research arose in near 2015. In addition, the Chinese literature from CNKI is relatively sparse and shows a very obvious rise after 2020, exceeding the number of related articles from WOS at this stage.  
In order to obtain a full picture of the breadth and depth of the literature, a VOSviewer analysis was subsequently performed, which was displayed as a colorful network of labels using the \"co-occurrence\" function in VOSviewer. The larger the node circles in the graph, the higher the frequency of the corresponding keywords, indicating that the keywords are the main research topics. The closer the node circles are, the higher the frequency of their co-occurrence in the same literature. The color of each node indicates time, and the gradient color bar in the lower-right corner marks the time course from blue to yellow, indicating the change in the keyword frequency over time. The co-occurrence time zone map effectively illustrates annual research hotspots and their trends over time.  
We analyze the co-occurrence of keywords in the retrieved 3032 documents, as in Figure 4, which can more intuitively and clearly describe the research hotspots in the field.  
From the map, it can be found that the blue keywords with the highest frequency are health, systems engineering, fault detection, condition-based maintenance, decision-making, and condition monitoring. Therefore, in the early stage, the research on PHM  
Figure 3: Trends in the number of articles per year.  
by scholars all over the world focused on the condition-based maintenance of equipment systems and the traditional methods of fault detection. The most frequent yellow keywords are deep learning, forecasting, learning systems, learning algorithms, long short-term memory, and battery management systems. This means that the emergence of various deep learning models has greatly promoted PHM research. More and more scholars have begun to optimize the deep learning models and continuously improve their accuracy and generalization ability to achieve full life cycle monitoring and management of device systems. In addition, we also find that many people are currently applying PHM combined with deep learning to the monitoring of batteries.