Rail vehicle door sub-health state recognition method based on time series data mining

A rail vehicle and time series technology, applied in the direction of railway vehicle testing, etc., can solve the problems of large DC voltage ripple, high order requirements of RC low-pass filter, etc., and achieve the effect of good application prospects.

Active Publication Date: 2017-11-14
NANJING KANGNI MECHANICAL & ELECTRICAL
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Problems solved by technology

[0005] The purpose of the present invention is to overcome the existing PWM to DC (direct current) circuit, which requires a high order number of the RC low-pass filter, and the problem that the output DC voltage has relatively large ripples

Method used

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  • Rail vehicle door sub-health state recognition method based on time series data mining
  • Rail vehicle door sub-health state recognition method based on time series data mining
  • Rail vehicle door sub-health state recognition method based on time series data mining

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Embodiment Construction

[0042] The present invention will be further described below in conjunction with the accompanying drawings.

[0043] The sub-health state identification method of the rail vehicle door based on time series data mining of the present invention uses a multi-scale sliding window discrete characterization algorithm to perform data mining on the speed, torque and current of the rail vehicle door motor; through calculation and normal state The distance of the template curve is used as a feature, and principal component analysis is used to reduce its dimensionality, remove redundant information, and obtain low-dimensional features with better classification performance; then use a hierarchical sub-health status recognition model to classify various sub-health status The data is identified layer by layer, and finally the identification of the sub-health state in the process of opening and closing the door can be effectively extracted. figure 1 shown, including the following steps,

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Abstract

The invention discloses a rail vehicle door sub-health state recognition method based on time series data mining. A multi-scale sliding window discrete character algorithm is used to carry out data mining on rotation speed, torque and current of a rail vehicle door motor. Through calculating a distance from a template curve in a normal state as a characteristic and using principal component analysis to reduce a dimension, redundant information is removed, and a low dimensional characteristic with good classification performance is obtained. Then, a hierarchical sub-health state recognition model is used to identify a variety of sub-health data layer by layer from coarse to fine, finally the identification of a sub-health state in the process of opening and closing a door is realized, the characteristic of vehicle door motor time series data can be effectively extracted, common sub health in the process of opening and closing the door is accurately identified, and the method has a good application prospect.

Description

technical field [0001] The invention relates to the technical field of urban rail transit fault detection, in particular to a sub-health state identification method for rail vehicle doors based on time series data mining. Background technique [0002] With the continuous development of the national economy, my country's urbanization process is gradually accelerating. The rapid increase of urban population, the expansion of urban scale, and the high frequency of residents' travel and material exchange have caused the urban transportation system to face a severe situation. As a key mode of transportation in the comprehensive transportation system, urban rail transit, with its unique technical and economic advantages such as large transport capacity, high efficiency, and low energy consumption, shoulders an important mission in alleviating urban traffic congestion and social and economic development. According to the fault statistics in the subway operation process in recent y...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01M17/08
CPCG01M17/08
Inventor 支有冉薛钰曹劲然许志兴张伟史翔
Owner NANJING KANGNI MECHANICAL & ELECTRICAL
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