Highway electromechanical system fault classification method based on deep sparse memory model

A technology of highways and electromechanical systems, applied in the field of intelligent transportation, to achieve the effect of accurate detection and classification

Pending Publication Date: 2021-12-03
SOUTHEAST UNIV
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Problems solved by technology

Under the trend of continuous expansion of data volume and changing data structure forms of expressway electromechanical systems, the efficiency and mobility of methods based on characteristic curves and methods based on equipment monitoring gradually show disadvantages

Method used

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  • Highway electromechanical system fault classification method based on deep sparse memory model
  • Highway electromechanical system fault classification method based on deep sparse memory model
  • Highway electromechanical system fault classification method based on deep sparse memory model

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Embodiment Construction

[0032] The present invention will be further explained below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the following specific embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention.

[0033] Such as Figure 3-5 As shown, the fault classification method of expressway electromechanical system based on deep sparse memory model provided by the present invention comprises the following steps:

[0034] S1: Obtain the data collected by the data acquisition and monitoring system (SCADA) of the electromechanical system of the expressway, and perform data preprocessing;

[0035] It is specifically:

[0036] ① Obtain the electrical quantity data collected by a substation of the expressway electromechanical system at a frequency of 1 time per hour for a certain period of time;

[0037] ②Cluster and sort the data according to "monitoring substation. line or e...

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Abstract

The invention discloses a highway electromechanical system fault classification method based on a deep sparse memory model. The method comprises the steps: acquiring data collected by a data collection and monitoring system SCADA of an expressway electromechanical system, and carrying out the data preprocessing; constructing a tripping fault detection model based on a double-layer stack type sparse auto-encoder and an SVM, performing feature extraction and fault detection on the electrical quantity parameters of the highway electromechanical system, and outputting a fault moment; and constructing an improved FSS-LSTM network model, performing fault state classification on the fault moment data of the system, and outputting fault classes. Through close combination of sparse learning and the deep neural network, classification efficiency is improved, classification of various faults is realized more accurately, and technical support is provided for operation and maintenance of an electromechanical system in a highway scene.

Description

technical field [0001] The patent of the present invention relates to the field of intelligent transportation and intelligent high-speed research, and specifically relates to a fault classification method for expressway electromechanical systems based on a deep sparse memory model. Background technique [0002] With the continuous development of intelligent assisted driving and ETC free-flow tolling technology, more and more intelligent transportation electromechanical equipment is applied in expressways, and there is an increasing demand for perception and prediction of the health status of electromechanical systems of complex expressway bridges. Transportation technology presents new topics and challenges. Under the trend of continuous expansion of data volume and changing data structure forms of expressway electromechanical systems, the efficiency and mobility of methods based on characteristic curves and methods based on equipment monitoring gradually show disadvantages....

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06N3/04G06N3/08
CPCG06N3/088G06N3/044G06N3/045G06F18/2411
Inventor 赵池航刘洋钱子晨
Owner SOUTHEAST UNIV
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