Turnout abnormity early warning and fault diagnosis method

A fault diagnosis and turnout technology, applied in the field of rail transit, can solve the problems of threshold determination not being universal, the fault diagnosis method of turnout being stuck, blurred boundaries, etc., to reduce manual participation, reliable early warning and alarm, and improve efficiency.

Active Publication Date: 2018-08-17
TONGJI UNIV
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Problems solved by technology

However, the actual fault diagnosis methods for turnouts are only limited to manual judgment or simple threshold judgment.
Manual judgment mainly depends on the experience of experts, and the boundaries are blurred; and because of the same type of switch machine, there are also differences

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  • Turnout abnormity early warning and fault diagnosis method

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[0046] The present invention will be further described below in conjunction with the embodiments shown in the drawings.

[0047] A turnout abnormal early warning and fault diagnosis method, which is based on a large number of turnout monitoring curves, through comprehensive analysis through segmentation, standard action current curve extraction, feature analysis and other means to realize abnormal early warning and fault diagnosis. The method includes the following steps:

[0048] (1) For a particular turnout (ie turnout to be tested), select its standard operating current curve based on its historical data and obtain historical data statistical characteristics. The historical data statistical characteristics include the normal value range of key features, mainly including the operating current Normal range

[0049] (2) Use the clustering algorithm to segment the current curve of the action to be measured. If it cannot be segmented correctly, it is a fault and go to step (3), otherw...

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Abstract

The invention provides a turnout abnormity early warning and fault diagnosis method. The method comprises the following steps of (1) based on the historical data of a turnout to be tested, selecting astandard motion curve; (2) using a clustering algorithm to segment the motion curve to be tested; if correct segmentation can not be performed, turning to a step (3); otherwise, turning to a step (4); (3) comparing a power curve in the motion curve to be tested with the power curve of all fault samples, searching the fault sample which is most similar to the power curve, and affirming that the motion curve to be tested has a fault corresponding to the found fault sample; (4) for each segment of the motion curve to be tested, extracting a first kind of characteristics; and (5) comparing with the standard motion curve and determining whether the motion curve to be tested is abnormal or normal. Compared with an existing segmentation method, the method has universality; simultaneously, compared with the prior art, the selected standard curve is scientific and reliability is high; in addition, adaptive diagnosis is realized, a demand to a person is less and efficiency is high.

Description

technical field [0001] The invention belongs to the technical field of rail transit, and relates to an early warning and diagnosis method, in particular to an abnormal early warning and fault diagnosis method for a turnout. Background technique [0002] By the end of 2016, the total mileage of China's high-speed rail network had exceeded 20,000 kilometers, accounting for more than 60% of the total mileage of the world's high-speed rail. The increasing speed of high-speed rail also requires a more reliable rail system to ensure the safety and efficiency of transportation. As a key equipment of the high-speed rail system, the failure rate of turnouts accounts for the highest proportion of the entire signal failure, which affects the efficiency of high-speed rail transportation. At present, the switch machines used in speed-up turnouts are usually three-phase AC switch machines. Due to the loss of switch machine components, foreign matter jamming, weather effects, etc., the sw...

Claims

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Application Information

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IPC IPC(8): G06K9/62
CPCG06F18/2321G06F18/24
Inventor 欧冬秀薛睿唐茂杰李玮
Owner TONGJI UNIV
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