Application of Maximum Generalized Rayleigh Entropy in Diagnosing Train Air-conditioning Refrigerant Charge

Objective In diagnosing train air-conditioning refrigerant charge using machine learning methods, numerous features often appear, posing a dilemma in feature selection for diagnosis. Excessive feature selection leads to high algorithm resource costs, while insufficient feature selection results in poor learning of fault information, negatively affecting the diagnostic model performance. Currently, a commonly used method for feature selection is PCA (principal component analysis), and another less frequently cited method is MRE (Maximum Rayleigh Entropy). Both methods, however, face the problem of high resource costs due to excessive features. The research is specially carried out aiming to reduce resource costs and the effectiveness of these two methods is compared. Method For this purpose, the typical SVM (support vector machine) and K-means clustering model are selected for comparison, and a small sample of historical data is used to construct dimensionality reduction algorithm, instead of using the entire sample to construct MRE dimensionality reduction algorithm, representing an improvement to the algorithm under the same goal of reducing resource costs. MRE dimensionality reduction based on small historical data sample and the commonly used PCA dimensionality reduction are compared in terms of their performance on F1 score, accuracy rate, and time cost in K-means clustering and SVM models. Result & Conclusion The results indicate that the fault diagnosis and detection time resource costs of SVM machine using MRE is only 3% that of SVM model trained on original data. Whether using K-means clustering or SVM, the test data accuracy rate after using the projected data from MRE is closer to 100% compared to other models.