Title :
The neural network technology applied on fault detection of locomotive converter
Author :
Lin, Li-Xin ; Jiang, Xin-Hua ; Huang, Zhi-Wu
Author_Institution :
Sch. of Inf. Sci. & Eng., Central South Univ., Changsha, China
Abstract :
The paper analyzes the fault principle of the SS7E locomotive converter, and classifies the fault sorts of it. The paper describes a method of adopting wavelet analysis to extract energy eigenvector from the output voltage wave of the converter, and proposes a fault diagnosis method of the converter by using energy eigenvector and neural-network technology. The hardware frame and software design of the fault diagnosis system are discussed and the application scheme to diagnose the specific fault position of converter online by increasing a few monitoring points is proposed. The validity of the method is proved by computer simulation.
Keywords :
circuit analysis computing; convertors; eigenvalues and eigenfunctions; fault tolerant computing; locomotives; neural nets; rail traffic; traffic engineering computing; wavelet transforms; SS7E locomotive converter; energy eigenvector; fault detection; fault diagnosis method; neural network; wavelet analysis; Application software; Computerized monitoring; Fault detection; Fault diagnosis; Hardware; Neural networks; Paper technology; Software design; Voltage; Wavelet analysis; converter; energy eigenvector; neural network; wavelet;
Conference_Titel :
Control and Decision Conference (CCDC), 2010 Chinese
Conference_Location :
Xuzhou
Print_ISBN :
978-1-4244-5181-4
Electronic_ISBN :
978-1-4244-5182-1
DOI :
10.1109/CCDC.2010.5498737
