# A Method for Constructing a Synthesis Health Index for Metro Vehicle Wheelsets
## 1 Introduction  
As urbanization rapidly progresses in China, subways, a vital part of modern urban rail transit, are expanding in both scale and operational mileage. The components of metro vehicles directly influence the safety, reliability, and comfort of train operations. Among these components, wheelsets play a critical role as they support the train's weight and guide its movement. Any deterioration in the health status of wheelsets can significantly impact the train's stability, noise levels, and overall service life. Therefore, ensuring the health of wheelsets is crucial for the safe operation of metro systems. At present, the monitoring of metro vehicle wheelset health is primarily carried out through periodic manual inspections and static monitoring systems located at fixed points. However, these traditional methods face limitations, such as infrequent inspections, limited real-time monitoring capabilities, and insufficient accuracy. These shortcomings make traditional approaches unsuitable for modern metro operations, which require high-frequency monitoring and the capacity to handle large volumes of data. Recently, advances in sensor technology, wireless communication, the Internet of Things (IoT), and artificial intelligence have spurred the development of dynamic, intelligent monitoring methods. These methods aim to continuously assess the health of wheelsets and provide real-time alerts for potential failures. Internationally, several research teams have proposed various methods for monitoring wheelset health. For example, [PERSON] et al. [1] developed an intelligent system based on deep learning algorithms that can analyze and predict wheelset health status in real-time. Similarly, [PERSON] [2] introduced a health status prediction method using deep learning,achieving high accuracy in fault detection using sensor data. Despite these advancements, existing methods are limited in their ability to construct comprehensive health status indicators and effectively integrate dynamic data for analysis. Specifically, there is a notable gap in research regarding the handling of multi-source, multi-dimensional data to derive a more representative synthesis health index (SHI). This study addresses these gaps by proposing a new method for constructing a comprehensive health index to evaluate the health status of metro vehicle wheelsets. This method integrates multi-source data from various sensors to form a more accurate and reliable health assessment. Unlike traditional methods such as Principal Component Analysis (PCA), which excels in linear data handling but struggles with non-linearity, the proposed method can better capture complex relationships within the data. Additionally, while neural networks offer explicit metrics for health status prediction, they tend to operate as black-box models, lacking interpretability. In contrast, our method offers a more transparent approach, providing clearer insights into the factors influencing wheelset health. The findings of this research demonstrate that the proposed method significantly enhances the accuracy and timeliness of wheelset health monitoring, offering more reliable technical support for the safe operation of urban rail transit. This approach provides a practical solution for addressing the real-time and accuracy challenges currently faced in wheelset health status monitoring, positioning it as a more effective tool for modern metro systems. Research Status on the Construction Method for the Synthesis Health Index of Metro Vehicle Wheelsets  
The health index (HI) is a statistical or quantitative metric that describes the health status of a studied object and reflects its dynamic performance degradation trend. The accuracy of its construction significantly influences the prediction of the system's Remaining Useful Life (RUL) [3]. Based on different construction methods, the HI can be categorized into two types [4]: the physics health index (PHI) and the SHI, the latter of which is also referred to as the virtual health index (VHI).