# A Method for Constructing a Synthesis Health Index for Metro Vehicle Wheelsets
## 5 Conclusions  
This paper investigates assessment methods for the health status of subway vehicle wheelsets based on the HC algorithm and similarity analysis. First, it analyzes how feature selection metrics constructed on monotonicity, trendiness, and robustness can effectively enhance the quality of feature selection. The KPCA dimensionality reduction method further minimizes data redundancy and optimizes the data processing workflow. Subsequently, outliers are removed using the iForest algorithm, and health status is classified and subdivided based on the HC algorithm, resulting in a robust baseline model and a one-dimensional SHI. Instance verification demonstrates that the constructed SHI effectively characterizes the changing trends in wheelset health status, showcasing high robustness and identification accuracy. This research expands the application of SHI in health management for subway vehicle wheelsets, providing more reliable technical support for the safe operation of urban rail transit.  
The method for constructing the comprehensive health index (SHI) exhibits broad applicability and can be applied to other critical components in subway systems, as well as extended to other transportation sectors, such as high-speed trains and aerospace. However, the method's application does have certain prerequisites:  
1. There must be valid data support to ensure that the input data accurately reflect the true operating conditions of the system.
2. Given the limitations in computational resources and cost, real-time predictions of remaining component life should not demand excessively high time sensitivity. Thus, a balance between real-time performance and prediction accuracy must be carefully considered based on the specific situation.
3. Considering the significant differences in structure, operating environments, and performance metrics among various components and transportation fields, it is crucial to fully consider the system's characteristics when selecting sensor data. The selection and processing of input data should be adjusted according to specific requirements.  
The proper setting of these prerequisites ensures that the method can provide accurate health assessments and life predictions in diverse application scenarios, thus supporting intelligent management and maintenance optimization in transportation systems.  
However, the method does have certain limitations, particularly in its reliance on historical data. Nevertheless, when applied to different lines, this issue can be effectively addressed through techniques such as data migration, enabling cross-system adaptability.  
Furthermore, the implementation and deployment of this system may face a series of challenges. For example, during data transmission, significant noise may occur, potentially affecting the quality and accuracy of the data, which in turn could influence the prediction and assessment outcomes of the system.  
Conceptualization, M.L.; methodology, T.Z.; validation, T.Z.; formal analysis, T.Z.; writing--original draft preparation, T.Z.; writing--review and editing, J.D.; supervision, M.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflicts of interest.