A method for predicting the axle temperature by adopting layered multivariate stepwise regression analysis

A step-by-step regression and prediction equation technology, applied in special data processing applications, instruments, electrical digital data processing, etc., can solve problems such as sensor interference, shaft temperature detection system defects, and inability to collect shaft temperature information correctly.

Pending Publication Date: 2019-05-03
YANCHENG TEACHERS UNIV
View PDF1 Cites 9 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the shaft temperature detection system still has its defects
The reasons for these system failures are as follows: First, the logical structure design of the shaft temperature alarm is not perfect, so that the shaft temperature information cannot be collected correctly and the error information cannot be filtered; secondly, the se

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • A method for predicting the axle temperature by adopting layered multivariate stepwise regression analysis
  • A method for predicting the axle temperature by adopting layered multivariate stepwise regression analysis
  • A method for predicting the axle temperature by adopting layered multivariate stepwise regression analysis

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0047] see Figure 1-2 , in an embodiment of the present invention, a method for predicting shaft temperature using hierarchical multiple stepwise regression analysis is characterized in that it includes the following steps:

[0048] (1) Data preprocessing: For the axle temperature collected by the sensor and the related data of the factors affecting its change, due to the existence of vacant values ​​and data redundancy, it is necessary to preprocess the data. For the data in the data collection interval For the vacant value, the nearest interpolation method is used, that is, the previous value of the vacant value is used to supplement the vacant value. At the same time, all data need to be "standardized" to establish a relative coefficient matrix;

[0049] Among them, the relevant data of the factors that affect its change are several factors that are related to the shaft temperature through the Pearson coefficient. Specifically, in this embodiment, as a preference, the rel...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses a method for predicting the shaft temperature by adopting layered multivariate stepwise regression analysis. According to the hierarchical multivariate stepwise regression analysis method, a shaft temperature prediction equation is established by combining real-time data, collected during operation of a high-speed train, of relevant factors of shaft temperature change, andmeanwhile three factors, namely the environment temperature, the operation speed and the air spring load, which greatly influence the train shaft temperature, are screened out. A prediction result andan actual measurement result are compared and analyzed, the result shows that a prediction model established through layered multivariate stepwise regression analysis has high precision, the change trend of the axle temperature can be accurately reflected, and certain help is provided for analysis and prediction of the axle temperature of a train, timely discovery of axle faults and guarantee ofsafe operation of the train.

Description

technical field [0001] The invention relates to the technical field of axles, in particular to a method for predicting axle temperature by adopting layered multivariate stepwise regression analysis. Background technique [0002] The axle is an important part of a high-speed train and a key part to ensure the safe operation of the train. The temperature of the axle is the most direct response to the operating condition of the axle. Once the axle is burned or cut, it is easy to cause a major train accident. From the fused sensor installed on the train in the early 1980s to monitor the axle temperature, to the installation of the wireless axle temperature alarm on the train now, the axle temperature detection system is constantly developing and improving. However, the axle temperature detection system still has its defects. The reasons for these system failures are as follows: First, the logical structure design of the shaft temperature alarm is not perfect, so that the shaft ...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G06F17/50
Inventor 王作雷卢东祥史雪荣
Owner YANCHENG TEACHERS UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap