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On-line monitoring and fault early-warning system and method for traction electric transmission system of train

A fault warning, electric drive technology, applied in railway vehicle testing, neural learning methods, instruments, etc., can solve problems such as low fault diagnosis accuracy, single diagnosis object, and inability to quickly diagnose online and real-time.

Active Publication Date: 2017-01-18
BEIJING JIAOTONG UNIV
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Problems solved by technology

[0005] The present invention provides an online real-time high reliability and robust online monitoring and fault early warning system and method based on SOM neural network to solve the problem of low accuracy of fault diagnosis in the existing technical solutions, single diagnostic object and inability to quickly Defects diagnosed online in real time

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  • On-line monitoring and fault early-warning system and method for traction electric transmission system of train

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Embodiment Construction

[0074] The following will be combined with Figure 1-6 , to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

[0075] figure 1 The structural diagram of the online monitoring and fault early warning system based on the SOM neural network provided for the embodiment of the present invention, by analyzing the fault mechanisms of key equipment such as the PWM rectifier, traction inverter, and traction motor of the train traction electric drive system, the collection is sensitive to faults. of the system state.

[0076] First of all, the first layer is the signal detection module, which obtains the ...

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Abstract

The invention provides an on-line monitoring and fault early-warning system and method for a traction electric transmission system of a train. The system comprises a signal detection module, a lower computer, a host computer and a monitoring and early-warning result displaying module, wherein the signal detection module obtains the system quantity states to be monitored, classifies the system quantity states and transmits to the lower computer, the lower computer filters and pre-processes the system quantity states, and extracts the time domain characteristic information and frequency domain characteristic information of the system. The main characteristic information of the traction electric transmission system can be obtained by characteristic compression and dimension reduction through fuzzy logical reasoning and PCA principal component analysis. The main characteristic information is input to a SOMNN fault early-warning module, and calculated and processed by using the SOM neural network algorithm. On-line monitoring the current state of the traction electric transmission system of the train is realized, and early warning for future fault is given. Rapid real-time monitoring the state of a traction electric transmission system of a train and fault early-warning can be realized.

Description

technical field [0001] The invention relates to an online monitoring and fault early warning system and method for a train traction electric transmission system, in particular to an online monitoring and fault warning system based on a self-organizing feature map (SOM) neural network (NN) applied to a train traction electric transmission system Early warning systems and methods. Background technique [0002] With the rapid development of railways, how to ensure the safe and stable operation of trains has become an important issue, and the traction electric drive system is the main source of train power, real-time monitoring and fault warning of key state variables of the traction electric drive system is the guarantee The key to the safe and reliable operation of trains. [0003] Most of the traditional fault diagnosis methods are manual diagnosis, single diagnosis target, time-consuming, labor-intensive, low accuracy and reliability, thus affecting the system maintenance, ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01M17/08G06K9/62G06N3/04G06N3/08
CPCG06N3/04G06N3/088G01M17/08G06F18/2135G06F18/25
Inventor 刁利军孟苓辉王磊刘志刚徐春梅
Owner BEIJING JIAOTONG UNIV
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