The Research on the Detection Methods of EMU Break Valves Features

Chun Ling Guan

doi:10.4028/www.scientific.net/AMM.687-691.1034

Paper Titles

Inference for Burr-XII Masked System Lifetime Data in Step-Stress Partially Accelerated Life Test with Type-I Progressive Hybrid Censoring
p.1015

Numerical Analysis of Transducer Installation Effect on Ultrasonic Gas Flow Meter
p.1019

The Research on Weak Fault Time Domain Feature Extraction of Diesel Engine Instantaneous Speed
p.1026

Multi-Resolution WNN Fault Diagnosis Model Based on Unscented Kalman Filtering for Rotating Machinery
p.1030

The Research on the Detection Methods of EMU Break Valves Features
p.1034

The Design and Realization of Airborne Electrical Equipments’ Online Test System in the Environments of Mechanics
p.1038

Application of Correlation Dimension in Extraction of Power Plant Blower Bearing Fault Feature
p.1044

The GUI-Based Simulation Optimization Platform for Fault Grey Prediction of Diesel Engine
p.1049

Research on Acoustic Mechanical Fault Diagnosis Method of High Voltage Circuit Breaker Based on Improved EEMD
p.1054

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 687-691The Research on the Detection Methods of EMU Break...

The Research on the Detection Methods of EMU Break Valves Features

Abstract:

This paper focuses on the detection technology for Electric Multiple Units (EMU) break valves features. Aiming at the issues of EMU break valves features detection, this paper propose a kind of EMU break valves feature detection technology based on neural network algorithm which does not overly dependent on break valve characteristic parameters. The spatial function neural network algorithm is used to predict the EMU break valves features. The experiments illustrate the proposed algorithm can increase the detection accuracy with satisfactory effects in EMU break valves features detection.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 687-691)

Pages:

1034-1037

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.687-691.1034

Citation:

Cite this paper

Online since:

November 2014

Authors:

Chun Ling Guan

Keywords:

Features Detection, Neural Network, Spatial Space Functions

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Liang Jianquan Guo Xiaohang, Chen lei, SiLi, dong-xing wang. A bullet train braking control system design study [J]. Journal of China railway, 2014. 6: 67-69.

Google Scholar

[2] SiLi, Zhang Dong winter, Liang Jianquan Guo Xiaohang. The CRH380BL brake clamp unit analysis [J]. China science and technology aspect. 2014. 9: 58-59 Chapter.

Google Scholar

[3] Wu Huajun, blue letter, tgprojects tiger. Emu design and realization of the braking energy feedback absorption equipment [J]. Journal of electronic technology and software engineering. 2014. 9: 130-130.

Google Scholar

[4] Chen Yanxin Yang Shuo. For high speed emu technology analysis of brake system study [J]. China mechanical. 2014. 4: 17-18.

Google Scholar

[5] Xu Guangzhi. CRH380BL bullet train braking principle and common troubleshooting [J]. China mechanical: 2014. 4: 24 to 25.

Google Scholar

[6] Liu Changke. About CRH harmony emu braking system analysis [J]. Iron technology. 2014. 1: 35 and 36.

Google Scholar