Maglev train levitation system fault detection method based on data driving

A technology for maglev trains and system failures, which can be used in railway vehicle testing, complex mathematical operations, etc., and can solve problems such as high model accuracy requirements

Active Publication Date: 2020-08-14
NAT UNIV OF DEFENSE TECH +1
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Problems solved by technology

Traditional fault diagnosis methods are mainly based on model expansion, but they have higher requirements for model accuracy

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  • Maglev train levitation system fault detection method based on data driving
  • Maglev train levitation system fault detection method based on data driving
  • Maglev train levitation system fault detection method based on data driving

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Embodiment Construction

[0087] In order to enable those skilled in the art to better understand the technical solution of the present invention, the present invention will be described in further detail below in conjunction with the accompanying drawings and specific embodiments. And the features in the embodiments can be combined with each other.

[0088] figure 1 It is a flow chart of a data-driven fault detection method for a suspension system of a maglev train in the present invention. Such as figure 1 As shown, a data-driven fault detection method for the suspension system of the maglev train provided by the embodiment of the present invention includes the following steps: S1, using the data identification of the suspension system to obtain the residual generator; S2, according to the data of the suspension system under normal operation Statistical feature quantities related to fault detection are obtained through training; S3. Construct fault detection statistics according to sample data to p...

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Abstract

The invention discloses a maglev train levitation system fault detection method based on data driving. The maglev train levitation system fault detection method comprises the following steps: S1, obtaining a residual generator through levitation system data identification; S2, according to data training under normal work of a suspension system, obtaining statistical characteristic quantity relatedto fault detection; and S3, constructing fault detection statistics according to the sample data to perform fault detection. According to the diagnosis method, the support of a system model is not needed, the residual generator is directly identified according to the suspension system data, whether the fault occurs or not can be directly judged by comparing the input data with a threshold value set by the training data, the uncertainty caused by the model is reduced, and the fault detection is accurate.

Description

technical field [0001] The invention belongs to the technical field of maglev trains, and in particular relates to a data-driven fault detection method for the levitation system of the maglev train. Background technique [0002] With the popularization of maglev trains, the safety and reliability of the levitation system has attracted more and more attention. During the operation of the maglev train, once the suspension system fails, the train will not be able to operate. If the system failure can be detected quickly when the suspension system failure occurs, accidents will be avoided to a large extent. The traditional fault diagnosis method is mainly based on model development, but it requires high accuracy of the model. Therefore, how to accurately detect the failure of the suspension system without the support of the system model, so as to reduce the uncertainty caused by the model, has become an urgent problem to be solved by those skilled in the art. Contents of the...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/18G06F17/16G01M17/08
CPCG06F17/18G06F17/16G01M17/08
Inventor 王志强龙志强高明罗婕翟明达张丽崔玉萌
Owner NAT UNIV OF DEFENSE TECH
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