Train air conditioning unit fault diagnosing method based on multi-classification strategy

Abstract

The invention discloses a train air conditioning unit fault diagnosing method based on a multi-classification strategy. The diagnosing method comprises the steps of firstly calculating the distribution densities of all categories according to actual normal operation data and various fault operation data of a train air conditioning unit; then ranking based on the junction distribution densities; and finally utilizing the ranking results to establish a binary classification strategy tree based on a PSVM, learning and training to obtain a fault diagnosing model of the train air conditioning unit and utilizing the fault diagnosing model of the train air conditioning unit to diagnose and determine fault types of the train air conditioning unit. The train air conditioning unit fault diagnosing method based on the multi-classification strategy can effectively improve the fault diagnosing accuracy.

Description

technical field [0001] The invention belongs to the technical field of fault detection methods for train air-conditioning units, and relates to a fault diagnosis method for train air-conditioning units based on a multi-classification strategy. Background technique [0002] With the rapid development of passenger trains in modern society, train air conditioning units have been widely used in passenger trains. The airtightness of modern passenger trains is relatively good, which directly leads to poor air circulation in the compartments. Especially during the peak period of Spring Festival travel, the density of people in the trains is higher, which requires the train air conditioners to work long-term and without failure. However, the train air-conditioning unit operates under special conditions for a long time, such as: strong vibration when the train is running at high speed, and the air condition outside the train changes greatly. The train air-conditioning unit is very pr...

AI Technical Summary

Problems solved by technology

[0005] At present, there are mainly two types of methods for constructing SVM multi-class classifiers: one is the direct method, which directly modifies the objective function, combines the parameter solutions of multiple classification surfaces into one optimization problem, and solves the optimization problem The problem is "one-time" to achieve multi-class classification; this method seems simple, but its computational complexity is relatively high, it is difficult to implement, and it is only suitable for small problems

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